Package evaluation of Turing on Julia 1.13.0-DEV.1080 (ed57414aec*) started at 2025-09-05T01:07:19.057


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 10.05s


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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 v0.13.1
  [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 v7.20.0
  [13072b0f] + AxisAlgorithms v1.1.0
  [39de3d68] + AxisArrays v0.4.7
  [198e06fe] + BangBang v0.4.4
  [9718e550] + Baselet v0.1.1
  [76274a88] + Bijectors v0.15.10
  [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 v1.1.0
  [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 v7.2.1
  [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 v4.6.0
  [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 v0.4.34
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [08abe8d2] + PrettyTables v3.0.8
  [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 v3.36.0
  [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 v2.116.3
  [c0aeaf25] + SciMLOperators v1.7.1
  [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 v1.9.15
  [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 v0.3.38
  [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 v10.46.0+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 v1.67.0+0
  [3f19e933] + p7zip_jll v17.6.0+0
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

Installation completed after 7.46s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 569.69s


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

     Testing Turing
      Status `/tmp/jl_wnJDAN/Project.toml`
  [47edcb42] ADTypes v1.17.0
  [80f14c24] AbstractMCMC v5.7.2
  [7a57a42e] AbstractPPL v0.13.1
  [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 v0.15.10
  [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 v1.1.0
  [09f84164] HypothesisTests v0.11.5
  [6fdf6af0] LogDensityProblems v2.1.2
  [996a588d] LogDensityProblemsAD v1.13.1
  [c7f686f2] MCMCChains v7.2.1
  [86f7a689] NamedArrays v0.10.4
  [429524aa] Optim v1.13.2
  [7f7a1694] Optimization v4.6.0
  [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_wnJDAN/Manifest.toml`
  [47edcb42] ADTypes v1.17.0
  [621f4979] AbstractFFTs v1.5.0
  [80f14c24] AbstractMCMC v5.7.2
  [7a57a42e] AbstractPPL v0.13.1
  [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 v7.20.0
  [13072b0f] AxisAlgorithms v1.1.0
  [39de3d68] AxisArrays v0.4.7
  [198e06fe] BangBang v0.4.4
  [9718e550] Baselet v0.1.1
  [76274a88] Bijectors v0.15.10
  [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 v1.1.0
  [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 v7.2.1
  [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 v4.6.0
  [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 v0.4.34
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.0
  [08abe8d2] PrettyTables v3.0.8
  [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 v3.36.0
  [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 v2.116.3
  [c0aeaf25] SciMLOperators v1.7.1
  [53ae85a6] SciMLStructures v1.7.0
  [30f210dd] ScientificTypesBase v3.0.0
  [efcf1570] Setfield v1.1.2
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  [82ae8749] StatsAPI v1.7.1
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        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading.
     Testing Running tests...
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[ Info: [Turing]: progress logging is disabled globally
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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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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:1952 [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

======================================================================================
Information request received. A stacktrace will print followed by a 1.0 second profile.
--trace-compile is enabled during profile collection.
======================================================================================

cmd: /opt/julia/bin/julia 123 running 1 of 1

signal (10): User defined signal 1
unknown function (ip: 0x74f6eb50c061) at /lib/x86_64-linux-gnu/libc.so.6
unknown function (ip: 0x74f6eb50ee5c) at /lib/x86_64-linux-gnu/libc.so.6
malloc at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
operator new at /workspace/srcdir/gcc-14.2.0/libstdc++-v3/libsupc++/new_op.cc:50
_ZN4llvm13LexicalScopes23getOrCreateInlinedScopeEPKNS_12DILocalScopeEPKNS_10DILocationE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13LexicalScopes23getOrCreateInlinedScopeEPKNS_12DILocalScopeEPKNS_10DILocationE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13LexicalScopes23getOrCreateInlinedScopeEPKNS_12DILocalScopeEPKNS_10DILocationE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13LexicalScopes23getOrCreateInlinedScopeEPKNS_12DILocalScopeEPKNS_10DILocationE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13LexicalScopes23getOrCreateInlinedScopeEPKNS_12DILocalScopeEPKNS_10DILocationE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13LexicalScopes20extractLexicalScopesERNS_15SmallVectorImplISt4pairIPKNS_12MachineInstrES5_EEERNS_8DenseMapIS5_PNS_12LexicalScopeENS_12DenseMapInfoIS5_vEENS_6detail12DenseMapPairIS5_SB_EEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13LexicalScopes10initializeERKNS_15MachineFunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm18LiveDebugVariables7LDVImpl16computeIntervalsEv at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm18LiveDebugVariables7LDVImpl20runOnMachineFunctionERNS_15MachineFunctionEb at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm31LiveDebugVariablesWrapperLegacy20runOnMachineFunctionERNS_15MachineFunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm19MachineFunctionPass13runOnFunctionERNS_8FunctionE.part.0 at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13FPPassManager13runOnFunctionERNS_8FunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13FPPassManager11runOnModuleERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm6legacy15PassManagerImpl3runERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm3orc14SimpleCompilerclERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
operator() at /source/src/jitlayers.cpp:1612
addModule at /source/src/jitlayers.cpp:2093
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
jl_apply at /source/src/julia.h:2382 [inlined]
jl_f__apply_iterate at /source/src/builtins.c:870
_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:2382 [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:550
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:550
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:550
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:772
jl_eval_module_expr at /source/src/toplevel.c:195 [inlined]
jl_toplevel_eval_flex at /source/src/toplevel.c:657
jl_toplevel_eval_flex at /source/src/toplevel.c:712
ijl_toplevel_eval at /source/src/toplevel.c:784
ijl_toplevel_eval_in at /source/src/toplevel.c:829
eval at ./boot.jl:489
include_string at ./loading.jl:2865
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
_include at ./loading.jl:2925
include at ./Base.jl:309
IncludeInto at ./Base.jl:310
unknown function (ip: 0x74f6e9b12d12) 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:2382 [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:690
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:550
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:550
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:772
jl_toplevel_eval_flex at /source/src/toplevel.c:712
ijl_toplevel_eval at /source/src/toplevel.c:784
ijl_toplevel_eval_in at /source/src/toplevel.c:829
eval at ./boot.jl:489
include_string at ./loading.jl:2865
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
_include at ./loading.jl:2925
include at ./Base.jl:309
IncludeInto at ./Base.jl:310
jfptr_IncludeInto_36079.1 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:2382 [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:690
jl_interpret_toplevel_thunk at /source/src/interpreter.c:899
jl_toplevel_eval_flex at /source/src/toplevel.c:772
jl_toplevel_eval_flex at /source/src/toplevel.c:712
ijl_toplevel_eval at /source/src/toplevel.c:784
ijl_toplevel_eval_in at /source/src/toplevel.c:829
eval at ./boot.jl:489
exec_options at ./client.jl:296
_start at ./client.jl:563
jfptr__start_63312.1 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:2382 [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: 0x74f6eb49e249) 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)

#=   67.1 ms =# precompile(Tuple{typeof(Base.any), Base.Generator{Array{AbstractPPL.VarName{:variance, typeof(Base.identity)}, 1}, GibbsTests.var"#7#8"{AbstractPPL.VarName{:y, Accessors.IndexLens{Tuple{Int64}}}}}})

==============================================================
Profile collected. A report will print at the next yield point.
Disabling --trace-compile
==============================================================


======================================================================================
Information request received. A stacktrace will print followed by a 1.0 second profile.
--trace-compile is enabled during profile collection.
======================================================================================

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_44650.1 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:2382 [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.
Disabling --trace-compile
==============================================================

┌ 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 0x00007df42c1c4100 Total snapshots: 416. Utilization: 0%
   ╎416 @Base/task.jl:1137  wait_forever()
415╎ 416 @Base/task.jl:1200  wait()


[123] signal 15: Terminated
in expression starting at /home/pkgeval/.julia/packages/Turing/Avpxw/test/mcmc/gibbs.jl:35
_ZN4llvm8RAGreedy9tryAssignERKNS_12LiveIntervalERNS_15AllocationOrderERNS_15SmallVectorImplINS_8RegisterEEERKNS_8SmallSetIS7_Lj16ESt4lessIS7_EEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm8RAGreedy17selectOrSplitImplERKNS_12LiveIntervalERNS_15SmallVectorImplINS_8RegisterEEERNS_8SmallSetIS5_Lj16ESt4lessIS5_EEERNS_11SmallVectorISt4pairIPS2_NS_10MCRegisterEELj8EEEj at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm8RAGreedy13selectOrSplitERKNS_12LiveIntervalERNS_15SmallVectorImplINS_8RegisterEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm12RegAllocBase16allocatePhysRegsEv at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm8RAGreedy20runOnMachineFunctionERNS_15MachineFunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm19MachineFunctionPass13runOnFunctionERNS_8FunctionE.part.0 at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13FPPassManager13runOnFunctionERNS_8FunctionE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm13FPPassManager11runOnModuleERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm6legacy15PassManagerImpl3runERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm3orc14SimpleCompilerclERNS_6ModuleE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
operator() at /source/src/jitlayers.cpp:1612
addModule at /source/src/jitlayers.cpp:2093
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
jl_apply at /source/src/julia.h:2382 [inlined]
jl_f__apply_iterate at /source/src/builtins.c:870
_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:2382 [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:550
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:550
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:550
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:772
jl_eval_module_expr at /source/src/toplevel.c:195 [inlined]
jl_toplevel_eval_flex at /source/src/toplevel.c:657
jl_toplevel_eval_flex at /source/src/toplevel.c:712
ijl_toplevel_eval at /source/src/toplevel.c:784
ijl_toplevel_eval_in at /source/src/toplevel.c:829
eval at ./boot.jl:489
include_string at ./loading.jl:2865
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
_include at ./loading.jl:2925
include at ./Base.jl:309
IncludeInto at ./Base.jl:310
unknown function (ip: 0x74f6e9b12d12) 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:2382 [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:690
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:550
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:550
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:772
jl_toplevel_eval_flex at /source/src/toplevel.c:712
ijl_toplevel_eval at /source/src/toplevel.c:784
ijl_toplevel_eval_in at /source/src/toplevel.c:829
eval at ./boot.jl:489
include_string at ./loading.jl:2865
_jl_invoke at /source/src/gf.c:3985 [inlined]
ijl_apply_generic at /source/src/gf.c:4198
_include at ./loading.jl:2925
include at ./Base.jl:309
IncludeInto at ./Base.jl:310
jfptr_IncludeInto_36079.1 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:2382 [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:690
jl_interpret_toplevel_thunk at /source/src/interpreter.c:899
jl_toplevel_eval_flex at /source/src/toplevel.c:772
jl_toplevel_eval_flex at /source/src/toplevel.c:712
ijl_toplevel_eval at /source/src/toplevel.c:784
ijl_toplevel_eval_in at /source/src/toplevel.c:829
eval at ./boot.jl:489
exec_options at ./client.jl:296
_start at ./client.jl:563
jfptr__start_63312.1 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:2382 [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: 0x74f6eb49e249) 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: 1071947558 (Pool: 1071942139; Big: 5419); GC: 231

PkgEval terminated after 2726.01s: test duration exceeded the time limit