Package evaluation of Turing on Julia 1.13.0-DEV.1039 (6568124d5b*) started at 2025-08-27T00:44:07.904


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# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 7.55s


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# Installation
#

Installing Turing...
   Resolving package versions...
    Updating `~/.julia/environments/v1.13/Project.toml`
  [fce5fe82] + Turing v0.40.2
    Updating `~/.julia/environments/v1.13/Manifest.toml`
  [47edcb42] + ADTypes v1.17.0
  [621f4979] + AbstractFFTs v1.5.0
  [80f14c24] + AbstractMCMC v5.7.2
  [7a57a42e] + AbstractPPL v0.13.0
  [1520ce14] + AbstractTrees v0.4.5
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.3.0
  [0bf59076] + AdvancedHMC v0.8.1
  [5b7e9947] + AdvancedMH v0.8.8
  [576499cb] + AdvancedPS v0.7.0
  [b5ca4192] + AdvancedVI v0.4.1
  [66dad0bd] + AliasTables v1.1.3
  [dce04be8] + ArgCheck v2.5.0
  [4fba245c] + ArrayInterface v7.19.0
  [13072b0f] + AxisAlgorithms v1.1.0
  [39de3d68] + AxisArrays v0.4.7
  [198e06fe] + BangBang v0.4.4
  [9718e550] + Baselet v0.1.1
  [76274a88] + Bijectors v0.15.9
  [082447d4] + ChainRules v1.72.5
  [d360d2e6] + ChainRulesCore v1.26.0
  [0ca39b1e] + Chairmarks v1.3.1
  [9e997f8a] + ChangesOfVariables v0.1.10
  [861a8166] + Combinatorics v1.0.3
  [38540f10] + CommonSolve v0.2.4
  [bbf7d656] + CommonSubexpressions v0.3.1
  [34da2185] + Compat v4.18.0
  [a33af91c] + CompositionsBase v0.1.2
  [88cd18e8] + ConsoleProgressMonitor v0.1.2
  [187b0558] + ConstructionBase v1.6.0
  [a8cc5b0e] + Crayons v4.1.1
  [9a962f9c] + DataAPI v1.16.0
⌅ [864edb3b] + DataStructures v0.18.22
  [e2d170a0] + DataValueInterfaces v1.0.0
  [244e2a9f] + DefineSingletons v0.1.2
  [8bb1440f] + DelimitedFiles v1.9.1
  [b429d917] + DensityInterface v0.4.0
  [163ba53b] + DiffResults v1.1.0
  [b552c78f] + DiffRules v1.15.1
  [a0c0ee7d] + DifferentiationInterface v0.7.7
  [31c24e10] + Distributions v0.25.120
  [ced4e74d] + DistributionsAD v0.6.58
  [ffbed154] + DocStringExtensions v0.9.5
  [366bfd00] + DynamicPPL v0.37.1
  [cad2338a] + EllipticalSliceSampling v2.0.0
  [4e289a0a] + EnumX v1.0.5
  [e2ba6199] + ExprTools v0.1.10
  [55351af7] + ExproniconLite v0.10.14
  [7a1cc6ca] + FFTW v1.9.0
  [9aa1b823] + FastClosures v0.3.2
  [1a297f60] + FillArrays v1.13.0
  [6a86dc24] + FiniteDiff v2.28.1
  [f6369f11] + ForwardDiff v1.0.1
  [069b7b12] + FunctionWrappers v1.1.3
  [77dc65aa] + FunctionWrappersWrappers v0.1.3
  [d9f16b24] + Functors v0.5.2
  [46192b85] + GPUArraysCore v0.2.0
  [34004b35] + HypergeometricFunctions v0.3.28
  [22cec73e] + InitialValues v0.3.1
  [a98d9a8b] + Interpolations v0.16.2
  [8197267c] + IntervalSets v0.7.11
  [3587e190] + InverseFunctions v0.1.17
  [41ab1584] + InvertedIndices v1.3.1
  [92d709cd] + IrrationalConstants v0.2.4
  [c8e1da08] + IterTools v1.10.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [692b3bcd] + JLLWrappers v1.7.1
  [682c06a0] + JSON v0.21.4
  [ae98c720] + Jieko v0.2.1
  [5ab0869b] + KernelDensity v0.6.10
  [5be7bae1] + LBFGSB v0.4.1
  [b964fa9f] + LaTeXStrings v1.4.0
  [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1
  [6f1fad26] + Libtask v0.9.4
  [d3d80556] + LineSearches v7.4.0
  [6fdf6af0] + LogDensityProblems v2.1.2
  [996a588d] + LogDensityProblemsAD v1.13.1
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [e6f89c97] + LoggingExtras v1.1.0
  [c7f686f2] + MCMCChains v7.2.0
  [be115224] + MCMCDiagnosticTools v0.3.15
  [e80e1ace] + MLJModelInterface v1.12.0
  [1914dd2f] + MacroTools v0.5.16
  [dbb5928d] + MappedArrays v0.4.2
  [128add7d] + MicroCollections v0.2.0
  [e1d29d7a] + Missings v1.2.0
  [dbe65cb8] + MistyClosures v2.1.0
  [2e0e35c7] + Moshi v0.3.7
  [d41bc354] + NLSolversBase v7.10.0
  [77ba4419] + NaNMath v1.1.3
  [86f7a689] + NamedArrays v0.10.4
  [c020b1a1] + NaturalSort v1.0.0
  [6fe1bfb0] + OffsetArrays v1.17.0
  [429524aa] + Optim v1.13.2
  [3bd65402] + Optimisers v0.4.6
  [7f7a1694] + Optimization v4.5.0
  [bca83a33] + OptimizationBase v2.10.0
  [36348300] + OptimizationOptimJL v0.4.3
  [bac558e1] + OrderedCollections v1.8.1
  [90014a1f] + PDMats v0.11.35
  [d96e819e] + Parameters v0.12.3
  [69de0a69] + Parsers v2.8.3
  [85a6dd25] + PositiveFactorizations v0.2.4
  [d236fae5] + PreallocationTools v0.4.33
  [aea7be01] + PrecompileTools v1.3.2
  [21216c6a] + Preferences v1.5.0
⌅ [08abe8d2] + PrettyTables v2.4.0
  [33c8b6b6] + ProgressLogging v0.1.5
  [92933f4c] + ProgressMeter v1.11.0
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [74087812] + Random123 v1.7.1
  [e6cf234a] + RandomNumbers v1.6.0
  [b3c3ace0] + RangeArrays v0.3.2
  [c84ed2f1] + Ratios v0.4.5
  [c1ae055f] + RealDot v0.1.0
  [3cdcf5f2] + RecipesBase v1.3.4
  [731186ca] + RecursiveArrayTools v3.37.1
  [189a3867] + Reexport v1.2.2
  [ae029012] + Requires v1.3.1
  [79098fc4] + Rmath v0.8.0
  [f2b01f46] + Roots v2.2.10
  [7e49a35a] + RuntimeGeneratedFunctions v0.5.15
⌅ [26aad666] + SSMProblems v0.5.2
  [0bca4576] + SciMLBase v2.112.0
  [c0aeaf25] + SciMLOperators v1.6.0
  [53ae85a6] + SciMLStructures v1.7.0
  [30f210dd] + ScientificTypesBase v3.0.0
  [efcf1570] + Setfield v1.1.2
  [a2af1166] + SortingAlgorithms v1.2.2
  [9f842d2f] + SparseConnectivityTracer v1.0.1
  [dc90abb0] + SparseInverseSubset v0.1.2
  [0a514795] + SparseMatrixColorings v0.4.21
  [276daf66] + SpecialFunctions v2.5.1
  [171d559e] + SplittablesBase v0.1.15
  [90137ffa] + StaticArrays v1.9.14
  [1e83bf80] + StaticArraysCore v1.4.3
  [64bff920] + StatisticalTraits v3.5.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
  [2913bbd2] + StatsBase v0.34.6
  [4c63d2b9] + StatsFuns v1.5.0
  [892a3eda] + StringManipulation v0.4.1
  [09ab397b] + StructArrays v0.7.1
  [2efcf032] + SymbolicIndexingInterface v0.3.43
  [3783bdb8] + TableTraits v1.0.1
  [bd369af6] + Tables v1.12.1
  [5d786b92] + TerminalLoggers v0.1.7
  [28d57a85] + Transducers v0.4.84
  [fce5fe82] + Turing v0.40.2
  [3a884ed6] + UnPack v1.0.2
  [efce3f68] + WoodburyMatrices v1.0.0
  [700de1a5] + ZygoteRules v0.2.7
  [f5851436] + FFTW_jll v3.3.11+0
  [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0
  [81d17ec3] + L_BFGS_B_jll v3.0.1+0
  [856f044c] + MKL_jll v2025.2.0+0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [1317d2d5] + oneTBB_jll v2022.0.0+0
  [0dad84c5] + ArgTools v1.1.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [f43a241f] + Downloads v1.7.0
  [7b1f6079] + FileWatching v1.11.0
  [9fa8497b] + Future v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0
  [4af54fe1] + LazyArtifacts v1.11.0
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.3.0
  [44cfe95a] + Pkg v1.13.0
  [de0858da] + Printf v1.11.0
  [3fa0cd96] + REPL v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization v1.11.0
  [1a1011a3] + SharedArrays v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.11.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test v1.11.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] + LibCURL_jll v8.15.0+1
  [e37daf67] + LibGit2_jll v1.9.1+0
  [29816b5a] + LibSSH2_jll v1.11.3+1
  [14a3606d] + MozillaCACerts_jll v2025.8.12
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [458c3c95] + OpenSSL_jll v3.5.2+0
  [efcefdf7] + PCRE2_jll v10.45.0+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [83775a58] + Zlib_jll v1.3.1+2
  [3161d3a3] + Zstd_jll v1.5.7+1
  [8e850b90] + libblastrampoline_jll v5.13.1+0
  [8e850ede] + nghttp2_jll v1.66.0+0
  [3f19e933] + p7zip_jll v17.6.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 4.83s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 712.45s


################################################################################
# Testing
#

     Testing Turing
      Status `/tmp/jl_izyjh3/Project.toml`
  [47edcb42] ADTypes v1.17.0
  [80f14c24] AbstractMCMC v5.7.2
  [7a57a42e] AbstractPPL v0.13.0
  [5b7e9947] AdvancedMH v0.8.8
  [576499cb] AdvancedPS v0.7.0
  [b5ca4192] AdvancedVI v0.4.1
  [4c88cf16] Aqua v0.8.14
  [198e06fe] BangBang v0.4.4
  [76274a88] Bijectors v0.15.9
  [aaaa29a8] Clustering v0.15.8
  [861a8166] Combinatorics v1.0.3
  [31c24e10] Distributions v0.25.120
  [ced4e74d] DistributionsAD v0.6.58
  [bbc10e6e] DynamicHMC v3.5.1
  [366bfd00] DynamicPPL v0.37.1
  [26cc04aa] FiniteDifferences v0.12.32
  [f6369f11] ForwardDiff v1.0.1
  [09f84164] HypothesisTests v0.11.5
  [6fdf6af0] LogDensityProblems v2.1.2
  [996a588d] LogDensityProblemsAD v1.13.1
  [c7f686f2] MCMCChains v7.2.0
  [86f7a689] NamedArrays v0.10.4
  [429524aa] Optim v1.13.2
  [7f7a1694] Optimization v4.5.0
  [3e6eede4] OptimizationBBO v0.4.1
  [4e6fcdb7] OptimizationNLopt v0.3.2
  [36348300] OptimizationOptimJL v0.4.3
  [90014a1f] PDMats v0.11.35
  [37e2e3b7] ReverseDiff v1.16.1
  [276daf66] SpecialFunctions v2.5.1
  [860ef19b] StableRNGs v1.0.3
  [2913bbd2] StatsBase v0.34.6
  [4c63d2b9] StatsFuns v1.5.0
  [a759f4b9] TimerOutputs v0.5.29
  [fce5fe82] Turing v0.40.2
  [37e2e46d] LinearAlgebra v1.13.0
  [44cfe95a] Pkg v1.13.0
  [9a3f8284] Random v1.11.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_izyjh3/Manifest.toml`
  [47edcb42] ADTypes v1.17.0
  [621f4979] AbstractFFTs v1.5.0
  [80f14c24] AbstractMCMC v5.7.2
  [7a57a42e] AbstractPPL v0.13.0
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.3.0
  [0bf59076] AdvancedHMC v0.8.1
  [5b7e9947] AdvancedMH v0.8.8
  [576499cb] AdvancedPS v0.7.0
  [b5ca4192] AdvancedVI v0.4.1
  [66dad0bd] AliasTables v1.1.3
  [4c88cf16] Aqua v0.8.14
  [dce04be8] ArgCheck v2.5.0
  [4fba245c] ArrayInterface v7.19.0
  [13072b0f] AxisAlgorithms v1.1.0
  [39de3d68] AxisArrays v0.4.7
  [198e06fe] BangBang v0.4.4
  [9718e550] Baselet v0.1.1
  [76274a88] Bijectors v0.15.9
  [a134a8b2] BlackBoxOptim v0.6.3
  [fa961155] CEnum v0.5.0
  [a9c8d775] CPUTime v1.0.0
  [082447d4] ChainRules v1.72.5
  [d360d2e6] ChainRulesCore v1.26.0
  [0ca39b1e] Chairmarks v1.3.1
  [9e997f8a] ChangesOfVariables v0.1.10
  [aaaa29a8] Clustering v0.15.8
  [861a8166] Combinatorics v1.0.3
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.1
  [34da2185] Compat v4.18.0
  [a33af91c] CompositionsBase v0.1.2
  [88cd18e8] ConsoleProgressMonitor v0.1.2
  [187b0558] ConstructionBase v1.6.0
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
⌅ [864edb3b] DataStructures v0.18.22
  [e2d170a0] DataValueInterfaces v1.0.0
  [244e2a9f] DefineSingletons v0.1.2
  [8bb1440f] DelimitedFiles v1.9.1
  [b429d917] DensityInterface v0.4.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [a0c0ee7d] DifferentiationInterface v0.7.7
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.120
  [ced4e74d] DistributionsAD v0.6.58
  [ffbed154] DocStringExtensions v0.9.5
  [bbc10e6e] DynamicHMC v3.5.1
  [366bfd00] DynamicPPL v0.37.1
  [cad2338a] EllipticalSliceSampling v2.0.0
  [4e289a0a] EnumX v1.0.5
  [e2ba6199] ExprTools v0.1.10
  [55351af7] ExproniconLite v0.10.14
  [7a1cc6ca] FFTW v1.9.0
  [9aa1b823] FastClosures v0.3.2
  [1a297f60] FillArrays v1.13.0
  [6a86dc24] FiniteDiff v2.28.1
  [26cc04aa] FiniteDifferences v0.12.32
  [f6369f11] ForwardDiff v1.0.1
  [069b7b12] FunctionWrappers v1.1.3
  [77dc65aa] FunctionWrappersWrappers v0.1.3
  [d9f16b24] Functors v0.5.2
  [46192b85] GPUArraysCore v0.2.0
  [34004b35] HypergeometricFunctions v0.3.28
  [09f84164] HypothesisTests v0.11.5
  [22cec73e] InitialValues v0.3.1
  [a98d9a8b] Interpolations v0.16.2
  [8197267c] IntervalSets v0.7.11
  [3587e190] InverseFunctions v0.1.17
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.4
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [692b3bcd] JLLWrappers v1.7.1
  [682c06a0] JSON v0.21.4
  [ae98c720] Jieko v0.2.1
  [5ab0869b] KernelDensity v0.6.10
  [5be7bae1] LBFGSB v0.4.1
  [b964fa9f] LaTeXStrings v1.4.0
  [1fad7336] LazyStack v0.1.3
  [1d6d02ad] LeftChildRightSiblingTrees v0.2.1
  [6f1fad26] Libtask v0.9.4
  [d3d80556] LineSearches v7.4.0
  [6fdf6af0] LogDensityProblems v2.1.2
  [996a588d] LogDensityProblemsAD v1.13.1
  [2ab3a3ac] LogExpFunctions v0.3.29
  [e6f89c97] LoggingExtras v1.1.0
  [c7f686f2] MCMCChains v7.2.0
  [be115224] MCMCDiagnosticTools v0.3.15
  [e80e1ace] MLJModelInterface v1.12.0
  [1914dd2f] MacroTools v0.5.16
  [dbb5928d] MappedArrays v0.4.2
  [128add7d] MicroCollections v0.2.0
  [e1d29d7a] Missings v1.2.0
  [dbe65cb8] MistyClosures v2.1.0
  [2e0e35c7] Moshi v0.3.7
  [d41bc354] NLSolversBase v7.10.0
  [76087f3c] NLopt v1.2.1
  [77ba4419] NaNMath v1.1.3
  [86f7a689] NamedArrays v0.10.4
  [c020b1a1] NaturalSort v1.0.0
  [b8a86587] NearestNeighbors v0.4.22
  [6fe1bfb0] OffsetArrays v1.17.0
  [429524aa] Optim v1.13.2
  [3bd65402] Optimisers v0.4.6
  [7f7a1694] Optimization v4.5.0
  [3e6eede4] OptimizationBBO v0.4.1
  [bca83a33] OptimizationBase v2.10.0
  [4e6fcdb7] OptimizationNLopt v0.3.2
  [36348300] OptimizationOptimJL v0.4.3
  [bac558e1] OrderedCollections v1.8.1
  [90014a1f] PDMats v0.11.35
  [65ce6f38] PackageExtensionCompat v1.0.2
  [d96e819e] Parameters v0.12.3
  [69de0a69] Parsers v2.8.3
  [85a6dd25] PositiveFactorizations v0.2.4
  [d236fae5] PreallocationTools v0.4.33
  [aea7be01] PrecompileTools v1.3.2
  [21216c6a] Preferences v1.5.0
⌅ [08abe8d2] PrettyTables v2.4.0
  [33c8b6b6] ProgressLogging v0.1.5
  [92933f4c] ProgressMeter v1.11.0
  [43287f4e] PtrArrays v1.3.0
  [1fd47b50] QuadGK v2.11.2
  [74087812] Random123 v1.7.1
  [e6cf234a] RandomNumbers v1.6.0
  [b3c3ace0] RangeArrays v0.3.2
  [c84ed2f1] Ratios v0.4.5
  [c1ae055f] RealDot v0.1.0
  [3cdcf5f2] RecipesBase v1.3.4
  [731186ca] RecursiveArrayTools v3.37.1
  [189a3867] Reexport v1.2.2
  [ae029012] Requires v1.3.1
  [37e2e3b7] ReverseDiff v1.16.1
  [708f8203] Richardson v1.4.2
  [79098fc4] Rmath v0.8.0
  [f2b01f46] Roots v2.2.10
  [7e49a35a] RuntimeGeneratedFunctions v0.5.15
⌅ [26aad666] SSMProblems v0.5.2
  [0bca4576] SciMLBase v2.112.0
  [c0aeaf25] SciMLOperators v1.6.0
  [53ae85a6] SciMLStructures v1.7.0
  [30f210dd] ScientificTypesBase v3.0.0
  [efcf1570] Setfield v1.1.2
  [a2af1166] SortingAlgorithms v1.2.2
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  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.7.1
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        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
[ Info: [Turing]: progress logging is disabled globally
Precompiling packages...
  34608.4 ms  ✓ ReverseDiff
  1 dependency successfully precompiled in 35 seconds. 32 already precompiled.
Precompiling packages...
  17441.8 ms  ✓ DistributionsAD → DistributionsADReverseDiffExt
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  15310.1 ms  ✓ Bijectors → BijectorsReverseDiffExt
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   9127.5 ms  ✓ LogDensityProblemsAD → LogDensityProblemsADReverseDiffExt
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[ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[s]
[ Info: Running AD on demo_assume_index_observe with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[m]
[ Info: Running AD on demo_assume_index_observe with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[s]
[ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[m]
[ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[s]
[ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[m]
[ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[s]
[ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[m]
[ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[s]
[ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[m]
[ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[s]
[ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[m]
[ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[s]
[ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[m]
[ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[s]
[ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[m]
[ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoForwardDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[s]
[ Info: Running AD on demo_assume_index_observe with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_index_observe, varnames=[m]
[ Info: Running AD on demo_assume_index_observe with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528044, 0.11182461275017186])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679435, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[s]
[ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe, varnames=[m]
[ Info: Running AD on demo_assume_multivariate_observe with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe_index with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[s]
[ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_dot_observe, varnames=[m]
[ Info: Running AD on demo_assume_dot_observe with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[s]
[ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_multivariate_observe_literal, varnames=[m]
[ Info: Running AD on demo_assume_multivariate_observe_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_index_literal, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe_index_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[s]
[ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_dot_observe_literal, varnames=[m]
[ Info: Running AD on demo_assume_dot_observe_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[s]
[ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_observe_literal, varnames=[m]
[ Info: Running AD on demo_assume_observe_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 1.8770958195241771]
       actual : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
     expected : (-7.959372701044623, [-2.782720277429388, -0.31582720472901354])
[ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[s]
[ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_assume_submodel_observe_index_literal, varnames=[m]
[ Info: Running AD on demo_assume_submodel_observe_index_literal with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.054266798927939, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528041, 0.11182461275017175])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_submodel, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe_submodel with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[s]
[ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_dot_assume_observe_matrix_index, varnames=[m]
[ Info: Running AD on demo_dot_assume_observe_matrix_index with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[s]
[ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])
[ Info: Testing Gibbs AD with model=demo_assume_matrix_observe_matrix_index, varnames=[m]
[ Info: Running AD on demo_assume_matrix_observe_matrix_index with ADTypes.AutoReverseDiff()
       params : [1.9092862731989177, 0.07458329596077654, -2.512781377651256, 0.9397581193848665]
       actual : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679439, 0.9669977815528041, 0.11182461275017175])
     expected : (-11.05426679892794, [-0.8945357945653385, 0.7158889078679436, 0.9669977815528044, 0.11182461275017186])

constructor: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/essential/container.jl:20
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/essential/container.jl:10
   [13] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [14] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/essential/container.jl:21 [inlined]
   [15] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [16] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/essential/container.jl:25 [inlined]
   [17] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [18] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [19] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [20] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:42 [inlined]
   [21] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [22] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [23] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [24] top-level scope
      @ none:6
   [25] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [26] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [27] _start()
      @ Base ./client.jl:563

fork: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/essential/container.jl:38
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/essential/container.jl:10
   [13] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [14] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/essential/container.jl:39 [inlined]
   [15] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [16] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/essential/container.jl:51 [inlined]
   [17] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [18] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [19] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [20] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:42 [inlined]
   [21] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [22] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [23] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [24] top-level scope
      @ none:6
   [25] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [26] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [27] _start()
      @ Base ./client.jl:563

models: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:27
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:200
   [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:186
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined]
   [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nparticles::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [25] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:161
   [27] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:149 [inlined]
   [28] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [29] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [30] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [32] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:13
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:28 [inlined]
   [35] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [36] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:36 [inlined]
   [37] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [38] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [39] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [40] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
   [41] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [42] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [43] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [44] top-level scope
      @ none:6
   [45] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [46] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [47] _start()
      @ Base ./client.jl:563

chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:53
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:200
   [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:186
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined]
   [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nparticles::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [25] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:161
   [27] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:149 [inlined]
   [28] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [29] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [30] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [31] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [32] test_chain_logp_metadata(spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}})
      @ Main.SamplerTestUtils ~/.julia/packages/Turing/Avpxw/test/test_utils/sampler.jl:18
   [33] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:13
   [34] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [35] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:54 [inlined]
   [36] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [37] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:54 [inlined]
   [38] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [39] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [41] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
   [42] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [43] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [44] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [45] top-level scope
      @ none:6
   [46] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [47] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [48] _start()
      @ Base ./client.jl:563

logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:57
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#62#63"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; nparticles::Int64, kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:200
   [16] kwcall(::@NamedTuple{initial_params::Nothing, nparticles::Int64}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:186
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{nparticles::Int64})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined]
   [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nparticles::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [25] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:161
   [27] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:149 [inlined]
   [28] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [29] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [30] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [32] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:13
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:58 [inlined]
   [35] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [36] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:70 [inlined]
   [37] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [38] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [39] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [40] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
   [41] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [42] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [43] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [44] top-level scope
      @ none:6
   [45] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [46] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [47] _start()
      @ Base ./client.jl:563

chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:99
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.LogNormal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined]
   [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [27] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [28] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [29] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [30] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [31] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [32] test_chain_logp_metadata(spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}})
      @ Main.SamplerTestUtils ~/.julia/packages/Turing/Avpxw/test/test_utils/sampler.jl:18
   [33] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:81
   [34] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [35] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:100 [inlined]
   [36] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [37] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:100 [inlined]
   [38] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [39] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [41] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
   [42] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [43] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [44] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [45] top-level scope
      @ none:6
   [46] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [47] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [48] _start()
      @ Base ./client.jl:563

logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:103
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{a::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:a, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:a, typeof(identity)}}, Vector{Float64}}, x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Bernoulli{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, BitVector}, b::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:b, typeof(identity)}, Int64}, Vector{Distributions.Gamma{Float64}}, Vector{AbstractPPL.VarName{:b, typeof(identity)}}, Vector{Float64}}, c::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:c, typeof(identity)}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:c, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined]
   [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [27] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [28] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [29] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [30] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [31] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [32] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:81
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:104 [inlined]
   [35] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [36] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:116 [inlined]
   [37] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [38] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [39] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [40] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
   [41] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [42] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [43] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [44] top-level scope
      @ none:6
   [45] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [46] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [47] _start()
      @ Base ./client.jl:563

reference particle: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:124
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined]
   [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [27] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [28] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [29] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [30] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [31] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [32] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:81
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:125 [inlined]
   [35] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [36] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:125 [inlined]
   [37] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [38] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [39] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [40] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
   [41] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [42] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [43] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [44] top-level scope
      @ none:6
   [45] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [46] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [47] _start()
      @ Base ./client.jl:563

addlogprob leads to reweighting: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:130
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:x, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined]
   [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [24] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [26] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [27] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [28] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [29] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22
   [30] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:81
   [31] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [32] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:133 [inlined]
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/particle_mcmc.jl:143 [inlined]
   [35] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [36] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [37] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [38] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
   [39] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [40] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [41] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [42] top-level scope
      @ none:6
   [43] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [44] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [45] _start()
      @ Base ./client.jl:563
┌ Warning: The model does not contain any parameters.
└ @ DynamicPPL.DebugUtils ~/.julia/packages/DynamicPPL/bXCZJ/src/debug_utils.jl:305
[ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.0598561011023344)
[ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.158304117994203)
[ Info: Testing emcee with large number of iterations
[ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 1.9758334846581072)
[ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.176829569918974)
[ Info: Starting ESS tests
[ Info: Starting ESS inference tests
[ Info: (symbol = :m, exact = 0.8, evaluated = 0.8172942592919593)
[ Info: (symbol = "m[1]", exact = 0.0, evaluated = -0.02456171083886478)
[ Info: (symbol = "m[2]", exact = 0.8, evaluated = 0.8075869528540663)

gdemo with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:55
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
     [1] getproperty
       @ ./Base_compiler.jl:57 [inlined]
     [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
     [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
     [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
     [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
     [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
     [7] #TapedTask#58
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
     [8] TapedTask
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
     [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
    [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
    [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
    [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
       @ Turing.Inference ./none:-1
    [13] iterate
       @ ./generator.jl:48 [inlined]
    [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
       @ Base ./array.jl:803
    [15] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
    [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
    [17] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
    [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
    [19] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
    [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
    [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
    [22] step
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
    [23] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
    [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64})()
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
    [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
       @ Base.CoreLogging ./logging/logging.jl:540
    [26] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
       @ Base.CoreLogging ./logging/logging.jl:651
    [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
    [28] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
    [29] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
    [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
    [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
    [32] sample
       @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
    [33] #sample#112
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
    [34] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}, N::Int64)
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22
    [35] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:14
    [36] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [37] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:42 [inlined]
    [38] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [39] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:56 [inlined]
    [40] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [41] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:57 [inlined]
    [42] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [43] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
    [44] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [45] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
    [46] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [47] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
    [48] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [49] top-level scope
       @ none:6
    [50] eval(m::Module, e::Any)
       @ Core ./boot.jl:489
    [51] exec_options(opts::Base.JLOptions)
       @ Base ./client.jl:296
    [52] _start()
       @ Base ./client.jl:563

MoGtest_default with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:61
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
     [1] getproperty
       @ ./Base_compiler.jl:57 [inlined]
     [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
     [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
     [4] build_callable(sig::Type{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
     [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
     [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
     [7] #TapedTask#58
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
     [8] TapedTask
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
     [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
    [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
    [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
    [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
       @ Turing.Inference ./none:-1
    [13] iterate
       @ ./generator.jl:48 [inlined]
    [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
       @ Base ./array.jl:803
    [15] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
    [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
    [17] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
    [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
    [19] gibbs_initialstep_recursive(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
    [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
    [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
    [22] step
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
    [23] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
    [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64})()
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
    [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
       @ Base.CoreLogging ./logging/logging.jl:540
    [26] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
       @ Base.CoreLogging ./logging/logging.jl:651
    [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
    [28] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
    [29] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
    [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
    [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
    [32] sample
       @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
    [33] #sample#112
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
    [34] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{3, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}, DynamicPPL.Sampler{Turing.Inference.ESS}}}, N::Int64)
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22
    [35] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:14
    [36] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [37] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:42 [inlined]
    [38] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [39] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:62 [inlined]
    [40] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [41] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/ess.jl:67 [inlined]
    [42] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [43] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
    [44] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [45] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:46 [inlined]
    [46] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [47] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
    [48] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [49] top-level scope
       @ none:6
    [50] eval(m::Module, e::Any)
       @ Core ./boot.jl:489
    [51] exec_options(opts::Base.JLOptions)
       @ Base ./client.jl:296
    [52] _start()
       @ Base ./client.jl:563
┌ Info: Found initial step size
└   ϵ = 3.2
┌ Info: Found initial step size
└   ϵ = 3.2
WARNING: Method definition (::Type{GibbsTests.Wrapper{T} where T})(T) where {T<:Real} in module GibbsTests at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:38 overwritten at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:200.
WARNING: Method definition (::Type{GibbsTests.Wrapper{T<:Real}})(Any) in module GibbsTests at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:38 overwritten at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:200.

Sampler call order: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:137
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
      [1] getproperty
        @ ./Base_compiler.jl:57 [inlined]
      [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
        @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
      [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
        @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
      [4] build_callable(sig::Type{Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}})
        @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
      [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
        @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
      [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
        @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
      [7] #TapedTask#58
        @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
      [8] TapedTask
        @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
      [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
        @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
     [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
        @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
     [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
        @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
     [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
        @ Turing.Inference ./none:-1
     [13] iterate
        @ ./generator.jl:48 [inlined]
     [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
        @ Base ./array.jl:803
     [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
        @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
     [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
        @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
     [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
        @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
     [18] step
        @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99 [inlined]
     [19] step(::Random.TaskLocalRNG, ::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, ::DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
        @ Main.GibbsTests ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:183
     [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), ::Random.TaskLocalRNG, ::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, ::DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})
        @ Main.GibbsTests ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:175
     [21] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}, Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}}; initial_params::Nothing, kwargs::@Kwargs{})
        @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
   ┌ [22] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}, Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}})
   │    @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   ├ [23] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}}; initial_params::Nothing, kwargs::@Kwargs{})
   │    @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:476
   ╰───── repeated 2 times
     [26] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
        @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
     [27] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}}}; initial_params::Nothing, kwargs::@Kwargs{})
        @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
     [28] step
        @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
     [29] macro expansion
        @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
     [30] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}}}, Int64, Float64, Int64, Int64})()
        @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
     [31] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
        @ Base.CoreLogging ./logging/logging.jl:540
     [32] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
        @ Base.CoreLogging ./logging/logging.jl:651
     [33] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
        @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
     [34] macro expansion
        @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
     [35] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
        @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
     [36] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}}}, N::Int64)
        @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
     [37] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
        @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
     [38] sample
        @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
     [39] #sample#112
        @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
     [40] sample
        @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
     [41] #sample#111
        @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
     [42] sample(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#759")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, DynamicPPL.Sampler{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}}, N::Int64)
        @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
     [43] top-level scope
        @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:140
     [44] macro expansion
        @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
     [45] macro expansion
        @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:246 [inlined]
     [46] include(mapexpr::Function, mod::Module, _path::String)
        @ Base ./Base.jl:309
     [47] top-level scope
        @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
     [48] macro expansion
        @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
     [49] macro expansion
        @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:54 [inlined]
     [50] macro expansion
        @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
     [51] macro expansion
        @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
     [52] include(mapexpr::Function, mod::Module, _path::String)
        @ Base ./Base.jl:309
     [53] top-level scope
        @ none:6
     [54] eval(m::Module, e::Any)
        @ Core ./boot.jl:489
     [55] exec_options(opts::Base.JLOptions)
        @ Base ./client.jl:296
     [56] _start()
        @ Base ./client.jl:563
[ Info: Starting Gibbs tests

Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:401
  Test threw exception
  Expression: sample(gdemo_default, s2, N) isa MCMCChains.Chains
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}, AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
   [19] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
   [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [21] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
   [22] step
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
   [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [26] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [28] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [29] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [31] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [32] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [33] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [34] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [35] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [36] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{1, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [37] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:379 [inlined]
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [41] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:401 [inlined]
   [42] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:742 [inlined]

Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:402
  Test threw exception
  Expression: sample(gdemo_default, s3, N) isa MCMCChains.Chains
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
   [19] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
   [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [21] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
   [22] step
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
   [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [26] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [28] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [29] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [31] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [32] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [33] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [34] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [35] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [36] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [37] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:379 [inlined]
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [41] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:402 [inlined]
   [42] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:742 [inlined]

Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:404
  Test threw exception
  Expression: sample(gdemo_default, s5, N) isa MCMCChains.Chains
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
   [19] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
   [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [21] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:476
   [22] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [23] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:476
   [24] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [25] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:476
   [26] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [27] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
   [28] step
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
   [29] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
   [30] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [31] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [32] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [33] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [34] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [35] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [36] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [37] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [38] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [39] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [40] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [41] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [42] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{5, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [43] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
   [44] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [45] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:379 [inlined]
   [46] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [47] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:404 [inlined]
   [48] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:742 [inlined]

Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:405
  Test threw exception
  Expression: sample(gdemo_default, s6, N) isa MCMCChains.Chains
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] step
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99 [inlined]
   [19] #step#1
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/repeat_sampler.jl:46 [inlined]
   [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/repeat_sampler.jl:40
   [21] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
   [22] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.HMCState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), DynamicPPL.LogDensityFunction{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, typeof(DynamicPPL.getlogjoint_internal), DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, ADTypes.AutoForwardDiff{1, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [23] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:476
   [24] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [25] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
   [26] step
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
   [27] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
   [28] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [29] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [30] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [31] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [32] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [33] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [34] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [35] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [36] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [37] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [38] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [39] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [40] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, Turing.Inference.RepeatSampler{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}}}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [41] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
   [42] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [43] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:379 [inlined]
   [44] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [45] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:405 [inlined]
   [46] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:742 [inlined]

Gibbs constructors: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:408
  Test threw exception
  Expression: sample(gdemo_default, g, N) isa MCMCChains.Chains
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
   [19] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
   [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
   [21] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
   [22] step
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
   [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [26] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [28] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [29] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [31] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [32] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [33] #sample#19
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:23 [inlined]
   [34] sample(model_or_logdensity::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMCDA{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N_or_isdone::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:20
   [35] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
   [36] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [37] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:379 [inlined]
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:408 [inlined]
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:742 [inlined]

CSMC and HMC on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:414
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
     [1] getproperty
       @ ./Base_compiler.jl:57 [inlined]
     [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
     [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
     [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
     [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
     [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
     [7] #TapedTask#58
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
     [8] TapedTask
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
     [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
    [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
    [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
    [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
       @ Turing.Inference ./none:-1
    [13] iterate
       @ ./generator.jl:48 [inlined]
    [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
       @ Base ./array.jl:803
    [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
    [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
    [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
    [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
    [19] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
    [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
    [21] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
    [22] step
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
    [23] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
    [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, Int64, Float64, Int64, Int64})()
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
    [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
       @ Base.CoreLogging ./logging/logging.jl:540
    [26] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
       @ Base.CoreLogging ./logging/logging.jl:651
    [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
    [28] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
    [29] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
    [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
    [31] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
    [32] sample
       @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
    [33] #sample#112
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
    [34] sample
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
    [35] #sample#111
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
    [36] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}, N::Int64)
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
    [37] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
    [38] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [39] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:414 [inlined]
    [40] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [41] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:415 [inlined]
    [42] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [43] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:416 [inlined]
    [44] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [45] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
    [46] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [47] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:54 [inlined]
    [48] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [49] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
    [50] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [51] top-level scope
       @ none:6
    [52] eval(m::Module, e::Any)
       @ Core ./boot.jl:489
    [53] exec_options(opts::Base.JLOptions)
       @ Base ./client.jl:296
    [54] _start()
       @ Base ./client.jl:563
┌ Info: Found initial step size
└   ϵ = 1.6
[ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.084520957726516)
[ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.186176744244979)

CSMC and ESS on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:428
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
     [1] getproperty
       @ ./Base_compiler.jl:57 [inlined]
     [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
     [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
     [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
     [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
     [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
     [7] #TapedTask#58
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
     [8] TapedTask
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
     [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
    [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
    [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
    [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
       @ Turing.Inference ./none:-1
    [13] iterate
       @ ./generator.jl:48 [inlined]
    [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
       @ Base ./array.jl:803
    [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
    [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
    [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
    [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:s, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
    [19] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
    [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
    [21] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
    [22] step
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
    [23] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
    [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64})()
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
    [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
       @ Base.CoreLogging ./logging/logging.jl:540
    [26] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
       @ Base.CoreLogging ./logging/logging.jl:651
    [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
    [28] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
    [29] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
    [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
    [31] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
    [32] sample
       @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
    [33] #sample#112
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
    [34] sample
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
    [35] #sample#111
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
    [36] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.ESS}}}, N::Int64)
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
    [37] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
    [38] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [39] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:414 [inlined]
    [40] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [41] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:429 [inlined]
    [42] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [43] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:430 [inlined]
    [44] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [45] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
    [46] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [47] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:54 [inlined]
    [48] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [49] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
    [50] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [51] top-level scope
       @ none:6
    [52] eval(m::Module, e::Any)
       @ Core ./boot.jl:489
    [53] exec_options(opts::Base.JLOptions)
       @ Base ./client.jl:296
    [54] _start()
       @ Base ./client.jl:563

CSMC on gdemo: Error During Test at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:435
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
    [1] getproperty
      @ ./Base_compiler.jl:57 [inlined]
    [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
    [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
      @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
    [7] #TapedTask#58
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
   [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo), DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.DefaultContext, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Float64, Float64}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
   [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
   [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
      @ Base ./array.jl:803
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
   [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
   [18] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:0 [inlined]
   [19] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
   [20] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [21] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
      @ Base.CoreLogging ./logging/logging.jl:651
   [22] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
   [24] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
   [25] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
   [26] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
   [27] sample
      @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
   [28] #sample#112
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
   [29] sample
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
   [30] #sample#111
      @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
   [31] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
   [32] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:414 [inlined]
   [35] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [36] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:436 [inlined]
   [37] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [38] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:437 [inlined]
   [39] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [40] top-level scope
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
   [41] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [42] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:54 [inlined]
   [43] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
   [44] macro expansion
      @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
   [45] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:309
   [46] top-level scope
      @ none:6
   [47] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [48] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:296
   [49] _start()
      @ Base ./client.jl:563

======================================================================================
Information request received. A stacktrace will print followed by a 1.0 second profile
======================================================================================

cmd: /opt/julia/bin/julia 335 running 1 of 1

signal (10): User defined signal 1
_ZNK4llvm6Module13getModuleFlagENS_9StringRefE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZNK4llvm6Module24getSemanticInterpositionEv at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZNK4llvm11GlobalValue14isInterposableEv at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN12_GLOBAL__N_18Verifier13visitCallBaseERN4llvm8CallBaseE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN12_GLOBAL__N_18Verifier13visitCallInstERN4llvm8CallInstE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN12_GLOBAL__N_18Verifier6verifyERKN4llvm8FunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm12verifyModuleERKNS_6ModuleEPNS_11raw_ostreamEPb at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
verifyLLVMIR at /source/src/pipeline.cpp:878
run at /source/src/llvm-remove-addrspaces.cpp:431
run at /source/src/llvm-remove-addrspaces.cpp:455
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91
_ZN4llvm11PassManagerINS_6ModuleENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/src/pipeline.cpp:791
operator() at /source/src/jitlayers.cpp:1510
withModuleDo<(anonymous namespace)::sizedOptimizerT<N>::operator()(llvm::orc::ThreadSafeModule) [with long unsigned int N = 4]::<lambda(llvm::Module&)> > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:136 [inlined]
operator() at /source/src/jitlayers.cpp:1471 [inlined]
operator() at /source/src/jitlayers.cpp:1623 [inlined]
addModule at /source/src/jitlayers.cpp:2080
jl_compile_codeinst_now at /source/src/jitlayers.cpp:682
jl_compile_codeinst_impl at /source/src/jitlayers.cpp:873
jl_compile_method_internal at /source/src/gf.c:3535
_jl_invoke at /source/src/gf.c:3993 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
#sample#56 at /home/pkgeval/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
sample at /home/pkgeval/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
#sample#112 at /home/pkgeval/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
sample at /home/pkgeval/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
#sample#111 at /home/pkgeval/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
sample at /home/pkgeval/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
unknown function (ip: 0x76653e59de6b) at (unknown file)
_jl_invoke at /source/src/gf.c:4001 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_body at /source/src/interpreter.c:581
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
jl_interpret_toplevel_thunk at /source/src/interpreter.c:899
jl_toplevel_eval_flex at /source/src/toplevel.c:773
jl_eval_module_expr at /source/src/toplevel.c:196 [inlined]
jl_toplevel_eval_flex at /source/src/toplevel.c:658
jl_toplevel_eval_flex at /source/src/toplevel.c:713
ijl_toplevel_eval at /source/src/toplevel.c:785
ijl_toplevel_eval_in at /source/src/toplevel.c:830
eval at ./boot.jl:489
include_string at ./loading.jl:2863
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
_include at ./loading.jl:2923
include at ./Base.jl:309
IncludeInto at ./Base.jl:310
unknown function (ip: 0x766558811fd2) at (unknown file)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_stmt_value at /source/src/interpreter.c:194 [inlined]
eval_body at /source/src/interpreter.c:708
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
jl_interpret_toplevel_thunk at /source/src/interpreter.c:899
jl_toplevel_eval_flex at /source/src/toplevel.c:773
jl_toplevel_eval_flex at /source/src/toplevel.c:713
ijl_toplevel_eval at /source/src/toplevel.c:785
ijl_toplevel_eval_in at /source/src/toplevel.c:830
eval at ./boot.jl:489
include_string at ./loading.jl:2863
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
_include at ./loading.jl:2923
include at ./Base.jl:309
IncludeInto at ./Base.jl:310
jfptr_IncludeInto_73519.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_stmt_value at /source/src/interpreter.c:194 [inlined]
eval_body at /source/src/interpreter.c:708
jl_interpret_toplevel_thunk at /source/src/interpreter.c:899
jl_toplevel_eval_flex at /source/src/toplevel.c:773
jl_toplevel_eval_flex at /source/src/toplevel.c:713
ijl_toplevel_eval at /source/src/toplevel.c:785
ijl_toplevel_eval_in at /source/src/toplevel.c:830
eval at ./boot.jl:489
exec_options at ./client.jl:296
_start at ./client.jl:563
jfptr__start_44071.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
true_main at /source/src/jlapi.c:971
jl_repl_entrypoint at /source/src/jlapi.c:1138
main at /source/cli/loader_exe.c:58
unknown function (ip: 0x766559bae249) at /lib/x86_64-linux-gnu/libc.so.6
__libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S
unknown function (ip: (nil)) at (unknown file)

==============================================================
Profile collected. A report will print at the next yield point
==============================================================


PG and HMC on MoGtest_default
======================================================================================
Information request received. A stacktrace will print followed by a 1.0 second profile
======================================================================================

cmd: /opt/julia/bin/julia 1 running 0 of 1

signal (10): User defined signal 1
epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404
uv_run at /workspace/srcdir/libuv/src/unix/core.c:430
ijl_task_get_next at /source/src/scheduler.c:457
wait at ./task.jl:1200
wait_forever at ./task.jl:1137
jfptr_wait_forever_74938.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
start_task at /source/src/task.c:1253
unknown function (ip: (nil)) at (unknown file)
: 
==============================================================
Profile collected. A report will print at the next yield point
==============================================================

Error During Test┌ Warning: There were no samples collected in one or more groups.
│ This may be due to idle threads, or you may need to run your
│ program longer (perhaps by running it multiple times),
│ or adjust the delay between samples with `Profile.init()`.
└ @ Profile /opt/julia/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1362
Overhead ╎ [+additional indent] Count File:Line  Function
=========================================================
Thread 1 (default) Task 0x0000795930d10c40 Total snapshots: 510. Utilization: 0%
   ╎510 @Base/task.jl:1137  wait_forever()
509╎ 510 @Base/task.jl:1200  wait()

┌ Warning: There were no samples collected in one or more groups.
│ This may be due to idle threads, or you may need to run your
│ program longer (perhaps by running it multiple times),
│ or adjust the delay between samples with `Profile.init()`.
└ @ Profile /opt/julia/share/julia/stdlib/v1.13/Profile/src/Profile.jl:1362
 at Overhead ╎ [+additional indent] Count File:Line  Function
=========================================================
Thread 1 (default) Task 0x000076653f800010 Total snapshots: 16. Utilization: 100%
  ╎14 @Base/client.jl:563  _start()
  ╎ 14 @Base/client.jl:296  exec_options(opts::Base.JLOptions)
  ╎  14 @Base/boot.jl:489  eval(m::Module, e::Any)
  ╎   14 @Base/Base.jl:310  (::Base.IncludeInto)(fname::String)
  ╎    14 @Base/Base.jl:309  include(mapexpr::Function, mod::Module, _path::Str…
  ╎     14 @Base/loading.jl:2923  _include(mapexpr::Function, mod::Module, _pat…
  ╎    ╎ 14 @Base/loading.jl:2863  include_string(mapexpr::typeof(identity), mo…
  ╎    ╎  14 @Base/boot.jl:489  eval(m::Module, e::Any)
  ╎    ╎   14 @Base/Base.jl:310  (::Base.IncludeInto)(fname::String)
  ╎    ╎    14 @Base/Base.jl:309  include(mapexpr::Function, mod::Module, _path…
  ╎    ╎     14 @Base/loading.jl:2923  _include(mapexpr::Function, mod::Module,…
  ╎    ╎    ╎ 14 @Base/loading.jl:2863  include_string(mapexpr::typeof(identity…
  ╎    ╎    ╎  14 @Base/boot.jl:489  eval(m::Module, e::Any)
  ╎    ╎    ╎   14 @Turing/…actmcmc.jl:16  sample(model::DynamicPPL.Model{typeo…
  ╎    ╎    ╎    14 @Turing/…actmcmc.jl:19  #sample#111
  ╎    ╎    ╎     14 @Turing/…ctmcmc.jl:22  sample
  ╎    ╎    ╎    ╎ 14 @Turing/…ctmcmc.jl:31  #sample#112
  ╎    ╎    ╎    ╎  14 @DynamicPPL/…er.jl:83  sample
13╎    ╎    ╎    ╎   14 @DynamicPPL/…r.jl:93  sample(rng::Random.TaskLocalRNG, …

/home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:441
  Got exception outside of a @test
  FieldError: type Compiler.IRCode has no field `linetable`, available fields: `stmts`, `argtypes`, `sptypes`, `debuginfo`, `cfg`, `new_nodes`, `meta`, `valid_worlds`
  Stacktrace:
     [1] getproperty
       @ ./Base_compiler.jl:57 [inlined]
     [2] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode, new_blocks::Vector{Libtask.BasicBlockCode.BBlock})
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:152
     [3] Libtask.BasicBlockCode.BBCode(ir::Compiler.IRCode)
       @ Libtask.BasicBlockCode ~/.julia/packages/Libtask/Sf4tJ/src/bbcode.jl:289
     [4] build_callable(sig::Type{Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:89
     [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:282
     [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
       @ Libtask ~/.julia/packages/Libtask/Sf4tJ/src/copyable_task.jl:279
     [7] #TapedTask#58
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:95 [inlined]
     [8] TapedTask
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:94 [inlined]
     [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
    [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.MoGtest), DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}, AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Matrix{Float64}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
    [11] AdvancedPS.Trace(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, sampler::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:510
    [12] (::Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
       @ Turing.Inference ./none:-1
    [13] iterate
       @ ./generator.jl:48 [inlined]
    [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#68#69"{DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
       @ Base ./array.jl:803
    [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::Nothing})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:325
    [16] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(DynamicPPL.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, vi::DynamicPPL.VarInfo{@NamedTuple{z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/particle_mcmc.jl:312
    [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:116
    [18] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(AbstractMCMC.step), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:z1, typeof(identity)}, AbstractPPL.VarName{:z2, typeof(identity)}, AbstractPPL.VarName{:z3, typeof(identity)}, AbstractPPL.VarName{:z4, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:99
    [19] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:459
    [20] kwcall(::@NamedTuple{initial_params::Nothing}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, samplers::Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}, vi::DynamicPPL.VarInfo{@NamedTuple{mu1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu1, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu1, typeof(identity)}}, Vector{Float64}}, mu2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mu2, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:mu2, typeof(identity)}}, Vector{Float64}}, z1::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z1, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z1, typeof(identity)}}, Vector{Int64}}, z2::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z2, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z2, typeof(identity)}}, Vector{Int64}}, z3::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z3, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z3, typeof(identity)}}, Vector{Int64}}, z4::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:z4, typeof(identity)}, Int64}, Vector{Distributions.Categorical{Float64, Vector{Float64}}}, Vector{AbstractPPL.VarName{:z4, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:429
    [21] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}; initial_params::Nothing, kwargs::@Kwargs{})
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:383
    [22] step
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/gibbs.jl:371 [inlined]
    [23] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:171 [inlined]
    [24] (::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, Int64, Float64, Int64, Int64})()
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:134
    [25] with_logstate(f::AbstractMCMC.var"#25#26"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
       @ Base.CoreLogging ./logging/logging.jl:540
    [26] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
       @ Base.CoreLogging ./logging/logging.jl:651
    [27] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:157
    [28] macro expansion
       @ ~/.julia/packages/AbstractMCMC/7f1oY/src/logging.jl:133 [inlined]
    [29] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:151
    [30] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64)
       @ AbstractMCMC ~/.julia/packages/AbstractMCMC/7f1oY/src/sample.jl:109
    [31] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{})
       @ DynamicPPL ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:93
    [32] sample
       @ ~/.julia/packages/DynamicPPL/bXCZJ/src/sampler.jl:83 [inlined]
    [33] #sample#112
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:31 [inlined]
    [34] sample
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:22 [inlined]
    [35] #sample#111
       @ ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:19 [inlined]
    [36] sample(model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, alg::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}}, Tuple{DynamicPPL.Sampler{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, DynamicPPL.Sampler{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}}}, N::Int64)
       @ Turing.Inference ~/.julia/packages/Turing/Avpxw/src/mcmc/abstractmcmc.jl:16
    [37] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:374
    [38] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [39] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:414 [inlined]
    [40] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [41] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:442 [inlined]
    [42] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [43] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:446 [inlined]
    [44] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [45] top-level scope
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:33
    [46] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [47] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:54 [inlined]
    [48] macro expansion
       @ /opt/julia/share/julia/stdlib/v1.13/Test/src/Test.jl:1962 [inlined]
    [49] macro expansion
       @ ~/.julia/packages/Turing/Avpxw/test/runtests.jl:25 [inlined]
    [50] include(mapexpr::Function, mod::Module, _path::String)
       @ Base ./Base.jl:309
    [51] top-level scope
       @ none:6
    [52] eval(m::Module, e::Any)
       @ Core ./boot.jl:489
    [53] exec_options(opts::Base.JLOptions)
       @ Base ./client.jl:296
    [54] _start()
       @ Base ./client.jl:563

[1] signal 15: Terminated
in expression starting at /PkgEval.jl/scripts/evaluate.jl:210
epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404
uv_run at /workspace/srcdir/libuv/src/unix/core.c:430
ijl_task_get_next at /source/src/scheduler.c:457
wait at ./task.jl:1200
wait_forever at ./task.jl:1137
jfptr_wait_forever_74938.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
start_task at /source/src/task.c:1253
unknown function (ip: (nil)) at (unknown file)
Allocations: 26077026 (Pool: 26076361; Big: 665); GC: 23

[335] signal 15: Terminated
in expression starting at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:373
apply_type_nothrow at ./range.jl:0
_builtin_nothrow at ./../usr/share/julia/Compiler/src/tfuncs.jl:2307
builtin_nothrow at ./../usr/share/julia/Compiler/src/tfuncs.jl:2781 [inlined]
builtin_effects at ./../usr/share/julia/Compiler/src/tfuncs.jl:2692
abstract_call_known at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2685
abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2903
abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:2896 [inlined]
abstract_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3056
abstract_eval_call at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3074 [inlined]
abstract_eval_statement_expr at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3430
abstract_eval_basic_statement at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3857 [inlined]
abstract_eval_basic_statement at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:3814 [inlined]
typeinf_local at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4363
jfptr_typeinf_local_86820.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
typeinf at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4521
typeinf_ext at ./../usr/share/julia/Compiler/src/typeinfer.jl:1385
typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1568 [inlined]
typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1577
jfptr_typeinf_ext_toplevel_85993.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
jl_type_infer at /source/src/gf.c:462
jl_compile_method_internal at /source/src/gf.c:3523
_jl_invoke at /source/src/gf.c:3993 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_body at /source/src/interpreter.c:581
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
jl_interpret_toplevel_thunk at /source/src/interpreter.c:899
jl_toplevel_eval_flex at /source/src/toplevel.c:773
jl_eval_module_expr at /source/src/toplevel.c:196 [inlined]
jl_toplevel_eval_flex at /source/src/toplevel.c:658
jl_toplevel_eval_flex at /source/src/toplevel.c:713
ijl_toplevel_eval at /source/src/toplevel.c:785
ijl_toplevel_eval_in at /source/src/toplevel.c:830
eval at ./boot.jl:489
include_string at ./loading.jl:2863
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
_include at ./loading.jl:2923
include at ./Base.jl:309
IncludeInto at ./Base.jl:310
unknown function (ip: 0x766558811fd2) at (unknown file)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_stmt_value at /source/src/interpreter.c:194 [inlined]
eval_body at /source/src/interpreter.c:708
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
jl_interpret_toplevel_thunk at /source/src/interpreter.c:899
jl_toplevel_eval_flex at /source/src/toplevel.c:773
jl_toplevel_eval_flex at /source/src/toplevel.c:713
ijl_toplevel_eval at /source/src/toplevel.c:785
ijl_toplevel_eval_in at /source/src/toplevel.c:830
eval at ./boot.jl:489
include_string at ./loading.jl:2863
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
_include at ./loading.jl:2923
include at ./Base.jl:309
IncludeInto at ./Base.jl:310
jfptr_IncludeInto_73519.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_stmt_value at /source/src/interpreter.c:194 [inlined]
eval_body at /source/src/interpreter.c:708
jl_interpret_toplevel_thunk at /source/src/interpreter.c:899
jl_toplevel_eval_flex at /source/src/toplevel.c:773
jl_toplevel_eval_flex at /source/src/toplevel.c:713
ijl_toplevel_eval at /source/src/toplevel.c:785
ijl_toplevel_eval_in at /source/src/toplevel.c:830
eval at ./boot.jl:489
exec_options at ./client.jl:296
_start at ./client.jl:563
jfptr__start_44071.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
jl_apply at /source/src/julia.h:2378 [inlined]
true_main at /source/src/jlapi.c:971
jl_repl_entrypoint at /source/src/jlapi.c:1138
main at /source/cli/loader_exe.c:58
unknown function (ip: 0x766559bae249) at /lib/x86_64-linux-gnu/libc.so.6
__libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S
unknown function (ip: (nil)) at (unknown file)
Allocations: 1231964144 (Pool: 1231957240; Big: 6904); GC: 245

PkgEval terminated after 2723.88s: test duration exceeded the time limit