Package evaluation to test Turing on Julia 1.14.0-DEV.87 (8bcaa34afa*) started at 2025-11-15T16:17:20.311


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

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 9.0s


################################################################################
# Installation
#

Installing Turing...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [fce5fe82] + Turing v0.41.1
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [47edcb42] + ADTypes v1.18.0
  [621f4979] + AbstractFFTs v1.5.0
  [80f14c24] + AbstractMCMC v5.10.0
  [7a57a42e] + AbstractPPL v0.13.6
  [1520ce14] + AbstractTrees v0.4.5
  [7d9f7c33] + Accessors v0.1.42
  [79e6a3ab] + Adapt v4.4.0
  [0bf59076] + AdvancedHMC v0.8.3
  [5b7e9947] + AdvancedMH v0.8.9
  [576499cb] + AdvancedPS v0.7.0
⌅ [b5ca4192] + AdvancedVI v0.4.1
  [66dad0bd] + AliasTables v1.1.3
  [dce04be8] + ArgCheck v2.5.0
  [4fba245c] + ArrayInterface v7.22.0
  [13072b0f] + AxisAlgorithms v1.1.0
  [39de3d68] + AxisArrays v0.4.8
  [198e06fe] + BangBang v0.4.6
  [9718e550] + Baselet v0.1.1
  [76274a88] + Bijectors v0.15.12
  [082447d4] + ChainRules v1.72.6
  [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 v4.18.1
  [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 v0.19.3
  [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 v0.7.11
  [31c24e10] + Distributions v0.25.122
  [ced4e74d] + DistributionsAD v0.6.58
  [ffbed154] + DocStringExtensions v0.9.5
  [366bfd00] + DynamicPPL v0.38.9
  [cad2338a] + EllipticalSliceSampling v2.0.0
  [4e289a0a] + EnumX v1.0.5
  [e2ba6199] + ExprTools v0.1.10
  [55351af7] + ExproniconLite v0.10.14
  [7a1cc6ca] + FFTW v1.10.0
  [9aa1b823] + FastClosures v0.3.2
  [1a297f60] + FillArrays v1.15.0
  [6a86dc24] + FiniteDiff v2.29.0
  [f6369f11] + ForwardDiff v1.3.0
  [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 v0.7.12
  [3587e190] + InverseFunctions v0.1.17
  [41ab1584] + InvertedIndices v1.3.1
  [92d709cd] + IrrationalConstants v0.2.6
  [c8e1da08] + IterTools v1.10.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [692b3bcd] + JLLWrappers v1.7.1
  [682c06a0] + JSON v1.3.0
  [ae98c720] + Jieko v0.2.1
  [5ab0869b] + KernelDensity v0.6.10
  [b964fa9f] + LaTeXStrings v1.4.0
  [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1
  [6f1fad26] + Libtask v0.9.10
  [d3d80556] + LineSearches v7.4.0
  [6fdf6af0] + LogDensityProblems v2.2.0
  [996a588d] + LogDensityProblemsAD v1.13.1
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [e6f89c97] + LoggingExtras v1.2.0
  [c7f686f2] + MCMCChains v7.6.0
  [be115224] + MCMCDiagnosticTools v0.3.15
  [e80e1ace] + MLJModelInterface v1.12.0
  [1914dd2f] + MacroTools v0.5.16
  [dbb5928d] + MappedArrays v0.4.2
  [128add7d] + MicroCollections v0.2.0
  [e1d29d7a] + Missings v1.2.0
  [dbe65cb8] + MistyClosures v2.1.0
  [2e0e35c7] + Moshi v0.3.7
  [d41bc354] + NLSolversBase v7.10.0
  [77ba4419] + NaNMath v1.1.3
  [86f7a689] + NamedArrays v0.10.5
  [c020b1a1] + NaturalSort v1.0.0
  [6fe1bfb0] + OffsetArrays v1.17.0
  [429524aa] + Optim v1.13.2
  [3bd65402] + Optimisers v0.4.6
  [7f7a1694] + Optimization v5.1.0
  [bca83a33] + OptimizationBase v4.0.2
  [36348300] + OptimizationOptimJL v0.4.8
  [bac558e1] + OrderedCollections v1.8.1
  [90014a1f] + PDMats v0.11.36
  [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 v3.1.0
  [33c8b6b6] + ProgressLogging v0.1.5
  [92933f4c] + ProgressMeter v1.11.0
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [74087812] + Random123 v1.7.1
  [e6cf234a] + RandomNumbers v1.6.0
  [b3c3ace0] + RangeArrays v0.3.2
  [c84ed2f1] + Ratios v0.4.5
  [c1ae055f] + RealDot v0.1.0
  [3cdcf5f2] + RecipesBase v1.3.4
  [731186ca] + RecursiveArrayTools v3.39.0
  [189a3867] + Reexport v1.2.2
  [ae029012] + Requires v1.3.1
  [79098fc4] + Rmath v0.9.0
  [f2b01f46] + Roots v2.2.10
  [7e49a35a] + RuntimeGeneratedFunctions v0.5.16
⌅ [26aad666] + SSMProblems v0.5.2
  [0bca4576] + SciMLBase v2.127.0
  [a6db7da4] + SciMLLogging v1.5.0
  [c0aeaf25] + SciMLOperators v1.11.0
  [431bcebd] + SciMLPublic v1.0.0
  [53ae85a6] + SciMLStructures v1.7.0
  [30f210dd] + ScientificTypesBase v3.0.0
  [efcf1570] + Setfield v1.1.2
  [a2af1166] + SortingAlgorithms v1.2.2
  [9f842d2f] + SparseConnectivityTracer v1.1.3
  [dc90abb0] + SparseInverseSubset v0.1.2
  [0a514795] + SparseMatrixColorings v0.4.23
  [276daf66] + SpecialFunctions v2.6.1
  [171d559e] + SplittablesBase v0.1.15
  [90137ffa] + StaticArrays v1.9.15
  [1e83bf80] + StaticArraysCore v1.4.4
  [64bff920] + StatisticalTraits v3.5.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
  [2913bbd2] + StatsBase v0.34.8
  [4c63d2b9] + StatsFuns v1.5.2
  [892a3eda] + StringManipulation v0.4.1
  [09ab397b] + StructArrays v0.7.2
  [ec057cc2] + StructUtils v2.6.0
  [2efcf032] + SymbolicIndexingInterface v0.3.46
  [3783bdb8] + TableTraits v1.0.1
  [bd369af6] + Tables v1.12.1
  [5d786b92] + TerminalLoggers v0.1.7
  [28d57a85] + Transducers v0.4.85
  [fce5fe82] + Turing v0.41.1
  [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
  [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+1
  [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 v1.0.0
  [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 v1.0.0
  [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 v8.16.0+0
  [e37daf67] + LibGit2_jll v1.9.1+0
  [29816b5a] + LibSSH2_jll v1.11.3+1
  [14a3606d] + MozillaCACerts_jll v2025.11.4
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [458c3c95] + OpenSSL_jll v3.5.4+0
  [efcefdf7] + PCRE2_jll v10.47.0+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 v5.15.0+0
  [8e850ede] + nghttp2_jll v1.68.0+1
  [3f19e933] + p7zip_jll v17.7.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 6.1s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

┌ Error: Failed to use TestEnv.jl; test dependencies will not be precompiled
│   exception =
│    UndefVarError: `project_rel_path` not defined in `TestEnv`
│    Suggestion: this global was defined as `Pkg.Operations.project_rel_path` but not assigned a value.
│    Stacktrace:
│     [1] get_test_dir(ctx::Pkg.Types.Context, pkgspec::PackageSpec)
│       @ TestEnv ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/common.jl:75
│     [2] test_dir_has_project_file
│       @ ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/common.jl:52 [inlined]
│     [3] maybe_gen_project_override!
│       @ ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/common.jl:83 [inlined]
│     [4] activate(pkg::String; allow_reresolve::Bool)
│       @ TestEnv ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/activate_set.jl:12
│     [5] activate(pkg::String)
│       @ TestEnv ~/.julia/packages/TestEnv/i9lgt/src/julia-1.11/activate_set.jl:9
│     [6] top-level scope
│       @ /PkgEval.jl/scripts/precompile.jl:24
│     [7] include(mod::Module, _path::String)
│       @ Base ./Base.jl:309
│     [8] exec_options(opts::Base.JLOptions)
│       @ Base ./client.jl:344
│     [9] _start()
│       @ Base ./client.jl:577
└ @ Main /PkgEval.jl/scripts/precompile.jl:26
Precompiling package dependencies...
Precompiling packages...
   2993.1 ms  ✓ LogDensityProblemsAD
   2622.6 ms  ✓ NamedArrays
   3787.8 ms  ✓ RecursiveArrayTools
   3880.4 ms  ✓ SymbolicIndexingInterface → SymbolicIndexingInterfacePrettyTablesExt
   4514.7 ms  ✓ AbstractMCMC
  10694.2 ms  ✓ Distributions
   6839.4 ms  ✓ Optim
   1158.4 ms  ✓ LogDensityProblemsAD → LogDensityProblemsADADTypesExt
   2949.7 ms  ✓ LogDensityProblemsAD → LogDensityProblemsADForwardDiffExt
   2542.9 ms  ✓ RecursiveArrayTools → RecursiveArrayToolsSparseArraysExt
   2708.6 ms  ✓ RecursiveArrayTools → RecursiveArrayToolsTablesExt
   1993.0 ms  ✓ RecursiveArrayTools → RecursiveArrayToolsForwardDiffExt
   1396.8 ms  ✓ RecursiveArrayTools → RecursiveArrayToolsStructArraysExt
  20939.4 ms  ✓ SciMLBase
   6716.2 ms  ✓ AdvancedHMC
   4625.4 ms  ✓ AbstractPPL
   4109.1 ms  ✓ Distributions → DistributionsTestExt
   3635.2 ms  ✓ Distributions → DistributionsDensityInterfaceExt
   3199.3 ms  ✓ Distributions → DistributionsChainRulesCoreExt
   5145.3 ms  ✓ MCMCDiagnosticTools
   1036.6 ms  ✓ LogDensityProblemsAD → LogDensityProblemsADDifferentiationInterfaceExt
   3139.2 ms  ✓ SciMLBase → SciMLBaseChainRulesCoreExt
   6088.6 ms  ✓ SciMLBase → SciMLBaseForwardDiffExt
   3221.9 ms  ✓ SciMLBase → SciMLBaseDifferentiationInterfaceExt
   4511.8 ms  ✓ SciMLBase → SciMLBaseDistributionsExt
   3739.1 ms  ✓ AdvancedHMC → AdvancedHMCADTypesExt
   6366.0 ms  ✓ AbstractPPL → AbstractPPLDistributionsExt
   4623.4 ms  ✓ SSMProblems
   5188.3 ms  ✓ AdvancedMH
   4819.5 ms  ✓ EllipticalSliceSampling
   4487.9 ms  ✓ KernelDensity
   4498.9 ms  ✓ AdvancedVI
   8171.5 ms  ✓ Bijectors
   8994.2 ms  ✓ DistributionsAD
   6150.8 ms  ✓ OptimizationBase
   4967.2 ms  ✓ AdvancedPS
   4198.8 ms  ✓ AdvancedMH → AdvancedMHStructArraysExt
   4421.8 ms  ✓ AdvancedMH → AdvancedMHForwardDiffExt
  10418.7 ms  ✓ MCMCChains
   3651.7 ms  ✓ Bijectors → BijectorsForwardDiffExt
  20782.8 ms  ✓ DynamicPPL
   3670.3 ms  ✓ AdvancedVI → AdvancedVIBijectorsExt
   3854.5 ms  ✓ DistributionsAD → DistributionsADForwardDiffExt
   3707.7 ms  ✓ Bijectors → BijectorsDistributionsADExt
   1406.0 ms  ✓ OptimizationBase → OptimizationForwardDiffExt
   4810.1 ms  ✓ Optimization
   4837.2 ms  ✓ AdvancedPS → AdvancedPSLibtaskExt
   7951.0 ms  ✓ AdvancedHMC → AdvancedHMCMCMCChainsExt
   7977.8 ms  ✓ AdvancedMH → AdvancedMHMCMCChainsExt
   5620.4 ms  ✓ DynamicPPL → DynamicPPLChainRulesCoreExt
   9326.7 ms  ✓ DynamicPPL → DynamicPPLMCMCChainsExt
   5486.3 ms  ✓ DynamicPPL → DynamicPPLForwardDiffExt
  29103.9 ms  ✓ OptimizationOptimJL
  16626.8 ms  ✓ Turing
  13345.8 ms  ✓ Turing → TuringOptimExt
  55 dependencies successfully precompiled in 340 seconds. 257 already precompiled.

Precompilation completed after 341.54s


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

     Testing Turing
        Test Could not use exact versions of packages in manifest, re-resolving. Note: if you do not check your manifest file into source control, then you can probably ignore this message. However, if you do check your manifest file into source control, then you probably want to pass the `allow_reresolve = false` kwarg when calling the `Pkg.test` function.
    Updating `/tmp/jl_6lGubq/Project.toml`
  [4c88cf16] + Aqua v0.8.14
  [aaaa29a8] + Clustering v0.15.8
  [bbc10e6e] + DynamicHMC v3.5.1
  [26cc04aa] + FiniteDifferences v0.12.33
  [09f84164] + HypothesisTests v0.11.6
  [3e6eede4] + OptimizationBBO v0.4.5
  [4e6fcdb7] + OptimizationNLopt v0.3.8
  [37e2e3b7] + ReverseDiff v1.16.1
  [860ef19b] + StableRNGs v1.0.4
  [a759f4b9] + TimerOutputs v0.5.29
  [fce5fe82] + Turing v0.41.1
    Updating `/tmp/jl_6lGubq/Manifest.toml`
  [0bf59076] + AdvancedHMC v0.8.3
  [4c88cf16] + Aqua v0.8.14
  [a134a8b2] + BlackBoxOptim v0.6.3
  [fa961155] + CEnum v0.5.0
  [a9c8d775] + CPUTime v1.0.0
  [aaaa29a8] + Clustering v0.15.8
  [b4f34e82] + Distances v0.10.12
  [bbc10e6e] + DynamicHMC v3.5.1
  [cad2338a] + EllipticalSliceSampling v2.0.0
  [26cc04aa] + FiniteDifferences v0.12.33
  [09f84164] + HypothesisTests v0.11.6
⌅ [682c06a0] ↓ JSON v1.3.0 ⇒ v0.21.4
  [1fad7336] + LazyStack v0.1.3
  [6f1fad26] + Libtask v0.9.10
  [dbe65cb8] + MistyClosures v2.1.0
  [76087f3c] + NLopt v1.2.1
  [b8a86587] + NearestNeighbors v0.4.23
  [3e6eede4] + OptimizationBBO v0.4.5
  [4e6fcdb7] + OptimizationNLopt v0.3.8
  [65ce6f38] + PackageExtensionCompat v1.0.2
  [37e2e3b7] + ReverseDiff v1.16.1
  [708f8203] + Richardson v1.4.2
  [d4ead438] + SpatialIndexing v0.1.6
  [860ef19b] + StableRNGs v1.0.4
  [5e0ebb24] + Strided v2.3.2
  [4db3bf67] + StridedViews v0.4.1
  [ec057cc2] - StructUtils v2.6.0
  [02d47bb6] + TensorCast v0.4.9
  [a759f4b9] + TimerOutputs v0.5.29
  [24ddb15e] + TransmuteDims v0.1.17
  [9d95972d] + TupleTools v1.6.0
  [fce5fe82] + Turing v0.41.1
  [079eb43e] + NLopt_jll v2.10.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`
        Test Successfully re-resolved
      Status `/tmp/jl_6lGubq/Project.toml`
  [47edcb42] ADTypes v1.18.0
  [80f14c24] AbstractMCMC v5.10.0
  [7a57a42e] AbstractPPL v0.13.6
  [5b7e9947] AdvancedMH v0.8.9
  [576499cb] AdvancedPS v0.7.0
⌅ [b5ca4192] AdvancedVI v0.4.1
  [4c88cf16] Aqua v0.8.14
  [198e06fe] BangBang v0.4.6
  [76274a88] Bijectors v0.15.12
  [aaaa29a8] Clustering v0.15.8
  [861a8166] Combinatorics v1.0.3
  [31c24e10] Distributions v0.25.122
  [ced4e74d] DistributionsAD v0.6.58
  [bbc10e6e] DynamicHMC v3.5.1
  [366bfd00] DynamicPPL v0.38.9
  [26cc04aa] FiniteDifferences v0.12.33
  [f6369f11] ForwardDiff v1.3.0
  [09f84164] HypothesisTests v0.11.6
  [6fdf6af0] LogDensityProblems v2.2.0
  [996a588d] LogDensityProblemsAD v1.13.1
  [c7f686f2] MCMCChains v7.6.0
  [86f7a689] NamedArrays v0.10.5
  [429524aa] Optim v1.13.2
  [7f7a1694] Optimization v5.1.0
  [3e6eede4] OptimizationBBO v0.4.5
  [4e6fcdb7] OptimizationNLopt v0.3.8
  [36348300] OptimizationOptimJL v0.4.8
  [90014a1f] PDMats v0.11.36
  [37e2e3b7] ReverseDiff v1.16.1
  [276daf66] SpecialFunctions v2.6.1
  [860ef19b] StableRNGs v1.0.4
  [2913bbd2] StatsBase v0.34.8
  [4c63d2b9] StatsFuns v1.5.2
  [a759f4b9] TimerOutputs v0.5.29
  [fce5fe82] Turing v0.41.1
  [37e2e46d] LinearAlgebra v1.13.0
  [44cfe95a] Pkg v1.13.0
  [9a3f8284] Random v1.11.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_6lGubq/Manifest.toml`
  [47edcb42] ADTypes v1.18.0
  [621f4979] AbstractFFTs v1.5.0
  [80f14c24] AbstractMCMC v5.10.0
  [7a57a42e] AbstractPPL v0.13.6
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
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        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
[ Info: [Turing]: progress logging is disabled globally
[ 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/lmRwJ/test/essential/container.jl:20
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#test#test##0", DynamicPPL.Model{Main.ContainerTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/essential/container.jl:10
   [13] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [14] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/essential/container.jl:21 [inlined]
   [15] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [16] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/essential/container.jl:25 [inlined]
   [17] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [18] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [19] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [20] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:42 [inlined]
   [21] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [22] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [23] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [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:310
   [27] _start()
      @ Base ./client.jl:577

fork: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/essential/container.jl:38
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [4] build_callable(sig::Type{Tuple{Main.ContainerTests.var"#normal#normal##0", DynamicPPL.Model{Main.ContainerTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/essential/container.jl:10
   [13] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [14] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/essential/container.jl:39 [inlined]
   [15] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [16] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/essential/container.jl:51 [inlined]
   [17] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [18] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [19] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [20] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:42 [inlined]
   [21] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [22] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [23] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [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:310
   [27] _start()
      @ Base ./client.jl:577
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
[ Info: Using a NamedTuple for `initial_params` will be deprecated in a future release. Please use `InitFromParams(namedtuple)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
[ Info: Using a Dict for `initial_params` will be deprecated in a future release. Please use `InitFromParams(dict)` instead.
┌ Warning: Only a single thread available: MCMC chains are not sampled in parallel
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:432
┌ Warning: Number of chains (10) is greater than number of samples per chain (1)
└ @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:437

models: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:27
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#normal#normal##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}}})
      @ Base ./array.jl:828
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:147
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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/lmRwJ/src/mcmc/particle_mcmc.jl:134
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{nparticles::Int64})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161 [inlined]
   [19] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [20] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [21] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [22] 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
   [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [24] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [26] kwcall(::@NamedTuple{chain_type::UnionAll, initial_params::DynamicPPL.InitFromPrior, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; check_model::Bool, chain_type::Type, initial_params::DynamicPPL.InitFromPrior, progress::Bool, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:121
   [28] sample
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:108 [inlined]
   [29] #sample#1
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:71 [inlined]
   [30] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#normal#normal##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:68
   [31] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:13
   [32] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [33] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:28 [inlined]
   [34] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [35] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:36 [inlined]
   [36] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [37] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [41] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [42] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [43] top-level scope
      @ none:6
   [44] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [45] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [46] _start()
      @ Base ./client.jl:577

chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:53
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [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{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}}})
      @ Base ./array.jl:828
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:147
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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/lmRwJ/src/mcmc/particle_mcmc.jl:134
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{nparticles::Int64})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161 [inlined]
   [19] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [20] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [21] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [22] 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
   [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [24] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [26] kwcall(::@NamedTuple{chain_type::UnionAll, initial_params::DynamicPPL.InitFromPrior, 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::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; check_model::Bool, chain_type::Type, initial_params::DynamicPPL.InitFromPrior, progress::Bool, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:121
   [28] sample
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:108 [inlined]
   [29] #sample#1
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:71 [inlined]
   [30] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:68
   [31] test_chain_logp_metadata(spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}})
      @ Main.SamplerTestUtils ~/.julia/packages/Turing/lmRwJ/test/test_utils/sampler.jl:22
   [32] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:13
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:54 [inlined]
   [35] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [36] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:54 [inlined]
   [37] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [38] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [39] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [40] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [41] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [42] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [43] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [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:310
   [47] _start()
      @ Base ./client.jl:577

logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:57
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##0", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}})(::Int64)
      @ Turing.Inference ./none:-1
   [13] iterate
      @ ./generator.jl:48 [inlined]
   [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#71#72"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}}})
      @ Base ./array.jl:828
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:147
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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/lmRwJ/src/mcmc/particle_mcmc.jl:134
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{nparticles::Int64})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161 [inlined]
   [19] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [20] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [21] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [22] 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
   [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [24] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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{initial_params::DynamicPPL.InitFromPrior, nparticles::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [26] kwcall(::@NamedTuple{chain_type::UnionAll, initial_params::DynamicPPL.InitFromPrior, progress::Bool, nparticles::Int64}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; check_model::Bool, chain_type::Type, initial_params::DynamicPPL.InitFromPrior, progress::Bool, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:121
   [28] sample
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:108 [inlined]
   [29] #sample#1
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:71 [inlined]
   [30] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.SMC{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:68
   [31] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:13
   [32] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [33] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:58 [inlined]
   [34] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [35] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:70 [inlined]
   [36] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [37] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [41] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [42] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [43] top-level scope
      @ none:6
   [44] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [45] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [46] _start()
      @ Base ./client.jl:577

chain log-density metadata: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:99
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [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{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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"#77#78"{DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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:828
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:264
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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/lmRwJ/src/mcmc/particle_mcmc.jl:257
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161 [inlined]
   [19] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [20] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [21] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [22] 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
   [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [24] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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{initial_params::DynamicPPL.InitFromPrior})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [26] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:85
   [28] sample
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:74 [inlined]
   [29] #sample#1
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:71 [inlined]
   [30] sample(model::DynamicPPL.Model{Main.SamplerTestUtils.var"#f#test_chain_logp_metadata##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:68
   [31] test_chain_logp_metadata(spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}})
      @ Main.SamplerTestUtils ~/.julia/packages/Turing/lmRwJ/test/test_utils/sampler.jl:22
   [32] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:81
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:100 [inlined]
   [35] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [36] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:100 [inlined]
   [37] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [38] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [39] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [40] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [41] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [42] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [43] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [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:310
   [47] _start()
      @ Base ./client.jl:577

logevidence: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:103
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [4] build_callable(sig::Type{Tuple{Main.ParticleMCMCTests.var"#test#test##1", DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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"#77#78"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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:828
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:264
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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/lmRwJ/src/mcmc/particle_mcmc.jl:257
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161 [inlined]
   [19] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [20] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [21] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [22] 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
   [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [24] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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{initial_params::DynamicPPL.InitFromPrior})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [26] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:85
   [28] sample
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:74 [inlined]
   [29] #sample#1
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:71 [inlined]
   [30] sample(model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#test#test##1", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:68
   [31] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:81
   [32] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [33] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:104 [inlined]
   [34] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [35] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:116 [inlined]
   [36] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [37] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [41] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [42] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [43] top-level scope
      @ none:6
   [44] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [45] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [46] _start()
      @ Base ./client.jl:577

reference particle: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:124
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.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{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}, Tuple{typeof(Main.Models.gdemo_d), DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.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"#77#78"{DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.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:828
   [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:264
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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/lmRwJ/src/mcmc/particle_mcmc.jl:257
   [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161 [inlined]
   [19] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [20] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [21] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [22] 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
   [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [24] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [25] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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{initial_params::DynamicPPL.InitFromPrior})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [26] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [27] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:85
   [28] sample
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:74 [inlined]
   [29] #sample#1
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:71 [inlined]
   [30] sample(model::DynamicPPL.Model{typeof(Main.Models.gdemo_d), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:68
   [31] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:81
   [32] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [33] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:125 [inlined]
   [34] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [35] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:125 [inlined]
   [36] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [37] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [40] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [41] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [42] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [43] top-level scope
      @ none:6
   [44] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [45] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [46] _start()
      @ Base ./client.jl:577

addlogprob leads to reweighting: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:130
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [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{}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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.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{}, Turing.Inference.ParticleMCMCContext{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{}, Turing.Inference.ParticleMCMCContext{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}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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"#77#78"{DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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:828
   [15] initialstep(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:264
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::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/lmRwJ/src/mcmc/particle_mcmc.jl:257
   [17] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161 [inlined]
   [19] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [20] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [21] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:540
   [22] 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
   [23] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [24] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [25] mcmcsample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, 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{initial_params::DynamicPPL.InitFromPrior})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [26] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [27] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:85
   [28] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{Main.ParticleMCMCTests.var"#addlogprob_demo#addlogprob_demo##0", (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:74
   [29] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:81
   [30] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [31] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:133 [inlined]
   [32] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [33] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/particle_mcmc.jl:143 [inlined]
   [34] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [35] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [36] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [37] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [40] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [41] top-level scope
      @ none:6
   [42] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [43] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [44] _start()
      @ Base ./client.jl:577
┌ Warning: The model does not contain any parameters.
└ @ DynamicPPL.DebugUtils ~/.julia/packages/DynamicPPL/Ut5Ls/src/debug_utils.jl:304
[ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.062630559118322)
[ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.1614954615219162)
[ Info: Testing emcee with large number of iterations
[ Info: (symbol = :s, exact = 2.0416666666666665, evaluated = 2.0765449812082557)
[ Info: (symbol = :m, exact = 1.1666666666666667, evaluated = 1.1697615669122199)
[ Info: Starting ESS tests
[ Info: Starting ESS inference tests
[ Info: (symbol = :m, exact = 0.8, evaluated = 0.8173726888512937)
[ Info: (symbol = "m[1]", exact = 0.0, evaluated = -0.025360856826163417)
[ Info: (symbol = "m[2]", exact = 0.8, evaluated = 0.8069838968220362)

gdemo with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:61
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.gdemo), 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}}}}}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.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{}, 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}}}}}, Turing.Inference.ParticleMCMCContext{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{}, 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}}}}}, Turing.Inference.ParticleMCMCContext{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.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{}, 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}}}}}, Turing.Inference.ParticleMCMCContext{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{}, 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}}}}}, Turing.Inference.ParticleMCMCContext{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}}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#77#78"{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.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"#77#78"{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.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:828
   [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::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:264
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.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::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/lmRwJ/src/mcmc/particle_mcmc.jl:257
   [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::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::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::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161
   [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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, 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::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:396
   [20] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, 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/lmRwJ/src/mcmc/gibbs.jl:373
   [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:328
   [22] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:317 [inlined]
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [24] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [25] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, 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/mcqES/src/logging.jl:157
   [28] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/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::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, 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{initial_params::DynamicPPL.InitFromPrior})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [30] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:85
   [32] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.gdemo), (:x, :y), (), (), Tuple{Float64, Float64}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.Gibbs{2, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}}, Tuple{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:74
   [33] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:14
   [34] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [35] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:48 [inlined]
   [36] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [37] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:62 [inlined]
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:63 [inlined]
   [40] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [41] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [42] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [43] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [44] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [45] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [46] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [47] top-level scope
      @ none:6
   [48] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [49] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [50] _start()
      @ Base ./client.jl:577

MoGtest_default with CSMC + ESS: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:67
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
    [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
    [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
    [3] optimise_ir!(ir::Compiler.IRCode)
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
    [4] build_callable(sig::Type{Tuple{typeof(Main.Models.MoGtest), 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}}}}}, Turing.Inference.ParticleMCMCContext{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/AGx8L/src/copyable_task.jl:98
    [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
    [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
      @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
    [7] #TapedTask#67
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
    [8] TapedTask
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
    [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{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{}, 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}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}}, Tuple{typeof(Main.Models.MoGtest), 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}}}}}, Turing.Inference.ParticleMCMCContext{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.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{}, 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}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}}, Tuple{typeof(Main.Models.MoGtest), 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}}}}}, Turing.Inference.ParticleMCMCContext{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}}, 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}}, resample::Bool)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
   [12] (::Turing.Inference.var"#77#78"{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.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"#77#78"{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.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:828
   [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::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::DynamicPPL.InitFromPrior})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:264
   [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.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::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/lmRwJ/src/mcmc/particle_mcmc.jl:257
   [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::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
   [18] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::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::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161
   [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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, 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::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:396
   [20] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, 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/lmRwJ/src/mcmc/gibbs.jl:373
   [21] step(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:328
   [22] step
      @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:317 [inlined]
   [23] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
   [24] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, 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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [25] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, StableRNGs.LehmerRNG, DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, 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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, 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/mcqES/src/logging.jl:157
   [28] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/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::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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, 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{initial_params::DynamicPPL.InitFromPrior})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [30] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, 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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [31] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{})
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:85
   [32] sample(rng::StableRNGs.LehmerRNG, model::DynamicPPL.Model{typeof(Main.Models.MoGtest), (:D,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::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{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, Turing.Inference.ESS, Turing.Inference.ESS}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:74
   [33] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:14
   [34] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [35] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:48 [inlined]
   [36] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [37] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:68 [inlined]
   [38] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [39] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/ess.jl:73 [inlined]
   [40] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [41] top-level scope
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
   [42] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [43] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:46 [inlined]
   [44] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [45] macro expansion
      @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
   [46] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [47] top-level scope
      @ none:6
   [48] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [49] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [50] _start()
      @ Base ./client.jl:577
┌ Info: Found initial step size
└   ϵ = 3.2
┌ Info: Found initial step size
└   ϵ = 3.2
WARNING: Method definition (::Type{GibbsTests.Wrapper{T} where T})(T) where {T<:Real} in module GibbsTests at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:38 overwritten at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:196.
WARNING: Method definition (::Type{GibbsTests.Wrapper{T<:Real}})(Any) in module GibbsTests at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:38 overwritten at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:196.

Sampler call order: Error During Test at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:138
  Got exception outside of a @test
  MethodError: no method matching Compiler.IRInterpretationState(::Compiler.NativeInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64, ::UInt64)
  The type `Compiler.IRInterpretationState` exists, but no method is defined for this combination of argument types when trying to construct it.
  Closest candidates are:
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, ::Compiler.SpecInfo, ::Compiler.IRCode, ::Core.MethodInstance, ::Vector{Any}, ::UInt64, ::UInt64)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:833
    Compiler.IRInterpretationState(::Compiler.AbstractInterpreter, !Matched::Core.CodeInstance, !Matched::Core.MethodInstance, !Matched::Vector{Any}, ::Any)
     @ Base /opt/julia/share/julia/Compiler/src/inferencestate.jl:866
  Stacktrace:
      [1] __infer_ir!(ir::Compiler.IRCode, interp::Compiler.NativeInterpreter, mi::Core.MethodInstance)
        @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:115
      [2] optimise_ir!(ir::Compiler.IRCode; show_ir::Bool, do_inline::Bool)
        @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:52
      [3] optimise_ir!(ir::Compiler.IRCode)
        @ Libtask ~/.julia/packages/Libtask/AGx8L/src/utils.jl:40
      [4] build_callable(sig::Type{Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}})
        @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:98
      [5] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any}; kwargs::@Kwargs{})
        @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:313
      [6] Libtask.TapedTask(::AdvancedPSLibtaskExt.TapedGlobals{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}, ::Function, ::Vararg{Any})
        @ Libtask ~/.julia/packages/Libtask/AGx8L/src/copyable_task.jl:303
      [7] #TapedTask#67
        @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:54 [inlined]
      [8] TapedTask
        @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:53 [inlined]
      [9] AdvancedPS.LibtaskModel(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}}, Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
        @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:38
     [10] AdvancedPS.Trace(::Turing.Inference.TracedModel{DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}}, Tuple{Main.GibbsTests.var"#test_model#test_model##1", DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, Turing.Inference.ParticleMCMCContext{AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}}}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, Int64, DynamicPPL.TypeWrap{Vector{Float64}}}}, ::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}})
        @ AdvancedPSLibtaskExt ~/.julia/packages/AdvancedPS/mkUwY/ext/AdvancedPSLibtaskExt.jl:84
     [11] AdvancedPS.Trace(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, varinfo::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}, rng::AdvancedPS.TracedRNG{UInt64, 1, Random123.Philox2x{UInt64, 10}}, resample::Bool)
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:454
     [12] (::Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}})(::Int64)
        @ Turing.Inference ./none:-1
     [13] iterate
        @ ./generator.jl:48 [inlined]
     [14] collect(itr::Base.Generator{UnitRange{Int64}, Turing.Inference.var"#77#78"{DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::Turing.Inference.ProduceLogLikelihoodAccumulator{Float64}}}}}})
        @ Base ./array.jl:828
     [15] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior})
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:264
     [16] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.initialstep), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}, vi::DynamicPPL.VarInfo{@NamedTuple{m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}})
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/particle_mcmc.jl:257
     [17] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, spl::Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:179
     [18] step
        @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:161 [inlined]
     [19] step(::Random.TaskLocalRNG, ::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, ::Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}; kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior})
        @ Main.GibbsTests ~/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:181
     [20] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(AbstractMCMC.step), ::Random.TaskLocalRNG, ::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, Turing.Inference.GibbsContext{Tuple{AbstractPPL.VarName{:m, typeof(identity)}}, Base.RefValue{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}}, DynamicPPL.DefaultContext}}, ::Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}})
        @ Main.GibbsTests ~/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:173
     [21] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}, Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:396
   ┌ [22] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}, Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}})
   │    @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:373
   ├ [23] gibbs_initialstep_recursive(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::typeof(AbstractMCMC.step), varname_vecs::Tuple{Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{Turing.Inference.MHState{DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, Float64}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
   │    @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:413
   ╰───── repeated 2 times
     [26] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior}, ::typeof(Turing.Inference.gibbs_initialstep_recursive), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, step_function::Function, varname_vecs::Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, samplers::Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}, vi::DynamicPPL.VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:s, typeof(identity)}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:m, typeof(identity)}, Int64}, Vector{Distributions.Poisson{Float64}}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{Int64}}, xs::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:xs, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, ys::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Beta{Float64}}, Vector{AbstractPPL.VarName{:ys, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}, q::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{Float64}}, r::DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}, Int64}, Vector{Distributions.Normal{Float64}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::DynamicPPL.LogPriorAccumulator{Float64}, LogJacobian::DynamicPPL.LogJacobianAccumulator{Float64}, LogLikelihood::DynamicPPL.LogLikelihoodAccumulator{Float64}}}}, states::Tuple{})
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:373
     [27] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}; initial_params::DynamicPPL.InitFromPrior, kwargs::@Kwargs{})
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:328
     [28] step
        @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:317 [inlined]
     [29] macro expansion
        @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:188 [inlined]
     [30] (::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, Int64, Float64, Int64, Int64})()
        @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
     [31] with_logstate(f::AbstractMCMC.var"#27#28"{Nothing, Int64, Int64, Int64, Type{MCMCChains.Chains}, Nothing, @Kwargs{initial_params::DynamicPPL.InitFromPrior}, Random.TaskLocalRNG, DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
        @ Base.CoreLogging ./logging/logging.jl:540
     [32] with_logger(f::Function, logger::LoggingExtras.TeeLogger{Tuple{LoggingExtras.EarlyFilteredLogger{TerminalLoggers.TerminalLogger, AbstractMCMC.var"#with_progresslogger##0#with_progresslogger##1"{Module}}, LoggingExtras.EarlyFilteredLogger{Base.CoreLogging.ConsoleLogger, AbstractMCMC.var"#with_progresslogger##2#with_progresslogger##3"{Module}}}})
        @ Base.CoreLogging ./logging/logging.jl:651
     [33] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
        @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
     [34] macro expansion
        @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
     [35] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{initial_params::DynamicPPL.InitFromPrior})
        @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
     [36] kwcall(::@NamedTuple{initial_params::DynamicPPL.InitFromPrior, chain_type::UnionAll}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, N::Int64)
        @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
     [37] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, N::Int64; initial_params::DynamicPPL.InitFromPrior, check_model::Bool, chain_type::Type, kwargs::@Kwargs{})
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:85
     [38] sample
        @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:74 [inlined]
     [39] #sample#1
        @ ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:71 [inlined]
     [40] sample(model::DynamicPPL.Model{Main.GibbsTests.var"#test_model#test_model##1", (:val, Symbol("##arg#801")), (), (), Tuple{Int64, DynamicPPL.TypeWrap{Vector{Float64}}}, Tuple{}, DynamicPPL.DefaultContext}, spl::Turing.Inference.Gibbs{12, Tuple{Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:m, typeof(identity)}}, Vector{AbstractPPL.VarName{:xs, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, typeof(identity)}}, Vector{AbstractPPL.VarName{:r, typeof(identity)}}, Vector{AbstractPPL.VarName{:ys, typeof(identity)}}, Vector{AbstractPPL.VarName{sym, typeof(identity)} where sym}, Vector{AbstractPPL.VarName{:s, typeof(identity)}}, Vector{AbstractPPL.VarName{:q, Accessors.PropertyLens{:a}}}, Vector{AbstractPPL.VarName{:r, Accessors.IndexLens{Tuple{Int64}}}}}, Tuple{Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.PG{AdvancedPS.ResampleWithESSThreshold{typeof(AdvancedPS.resample_systematic), Float64}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.NUTS{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.HMC{ADTypes.AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}, Main.GibbsTests.AlgWrapper{Turing.Inference.MH{@NamedTuple{}}}}}, N::Int64)
        @ Turing.Inference ~/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:68
     [41] top-level scope
        @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:141
     [42] macro expansion
        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
     [43] macro expansion
        @ ~/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:242 [inlined]
     [44] include(mapexpr::Function, mod::Module, _path::String)
        @ Base ./Base.jl:310
     [45] top-level scope
        @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:33
     [46] macro expansion
        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
     [47] macro expansion
        @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:55 [inlined]
     [48] macro expansion
        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
     [49] macro expansion
        @ ~/.julia/packages/Turing/lmRwJ/test/runtests.jl:25 [inlined]
     [50] include(mapexpr::Function, mod::Module, _path::String)
        @ Base ./Base.jl:310
     [51] top-level scope
        @ none:6
     [52] eval(m::Module, e::Any)
        @ Core ./boot.jl:489
     [53] exec_options(opts::Base.JLOptions)
        @ Base ./client.jl:310
     [54] _start()
        @ Base ./client.jl:577
[ Info: Starting Gibbs tests

======================================================================================
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 233 running 1 of 1

signal (10): User defined signal 1
compare_svec at /source/src/builtins.c:92
jl_egal__unboxed_ at /source/src/julia.h:1785 [inlined]
jl_egal_ at /source/src/julia.h:1795 [inlined]
literal_val_id at /source/src/ircode.c:144 [inlined]
jl_encode_as_indexed_root at /source/src/ircode.c:176
jl_encode_value_ at /source/src/ircode.c:546
jl_encode_value_ at /source/src/ircode.c:253 [inlined]
jl_encode_memory_slice at /source/src/ircode.c:213
jl_encode_value_ at /source/src/ircode.c:493
jl_encode_value_ at /source/src/ircode.c:253 [inlined]
ijl_compress_ir at /source/src/ircode.c:1072
maybe_compress_codeinfo at ./../usr/share/julia/Compiler/src/typeinfer.jl:507 [inlined]
finish! at ./../usr/share/julia/Compiler/src/typeinfer.jl:165
finish_nocycle at ./../usr/share/julia/Compiler/src/typeinfer.jl:285
jfptr_finish_nocycle_86946.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
typeinf at ./../usr/share/julia/Compiler/src/abstractinterpretation.jl:4502
typeinf_ext at ./../usr/share/julia/Compiler/src/typeinfer.jl:1527
typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1704 [inlined]
typeinf_ext_toplevel at ./../usr/share/julia/Compiler/src/typeinfer.jl:1713
jfptr_typeinf_ext_toplevel_88250.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [inlined]
jl_type_infer at /source/src/gf.c:463
jl_compile_method_internal at /source/src/gf.c:3636
_jl_invoke at /source/src/gf.c:4108 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
#gibbs_step_recursive#88 at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:619
gibbs_step_recursive at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:582 [inlined]
gibbs_step_recursive at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:582 [inlined]
#step#86 at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:439
step at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/gibbs.jl:426
unknown function (ip: 0x7aab595d5997) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:0 [inlined]
#27 at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
with_logstate at ./logging/logging.jl:540
unknown function (ip: 0x7aab595b3370) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
with_logger at ./logging/logging.jl:651
with_progresslogger at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
unknown function (ip: 0x7aab6bc046ae) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
macro expansion at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
#mcmcsample#25 at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
mcmcsample at /home/pkgeval/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
unknown function (ip: 0x7aab595b0c28) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
#sample#2 at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:85
sample at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:74 [inlined]
#sample#1 at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:71 [inlined]
sample at /home/pkgeval/.julia/packages/Turing/lmRwJ/src/mcmc/abstractmcmc.jl:68
unknown function (ip: 0x7aab595ad9d0) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [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:679
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:884
jl_toplevel_eval_flex at /source/src/toplevel.c:742
jl_eval_toplevel_stmts at /source/src/toplevel.c:585
jl_eval_module_expr at /source/src/toplevel.c:248 [inlined]
jl_toplevel_eval_flex at /source/src/toplevel.c:650
jl_eval_toplevel_stmts at /source/src/toplevel.c:585
jl_toplevel_eval_flex at /source/src/toplevel.c:683
ijl_toplevel_eval at /source/src/toplevel.c:754
ijl_toplevel_eval_in at /source/src/toplevel.c:799
eval at ./boot.jl:489
include_string at ./loading.jl:2994
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
_include at ./loading.jl:3054
include at ./Base.jl:310
IncludeInto at ./Base.jl:311
unknown function (ip: 0x7aab5f687e32) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [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:679
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:884
jl_toplevel_eval_flex at /source/src/toplevel.c:742
jl_eval_toplevel_stmts at /source/src/toplevel.c:585
jl_toplevel_eval_flex at /source/src/toplevel.c:683
ijl_toplevel_eval at /source/src/toplevel.c:754
ijl_toplevel_eval_in at /source/src/toplevel.c:799
eval at ./boot.jl:489
include_string at ./loading.jl:2994
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
_include at ./loading.jl:3054
include at ./Base.jl:310
IncludeInto at ./Base.jl:311
jfptr_IncludeInto_35320.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [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:679
jl_interpret_toplevel_thunk at /source/src/interpreter.c:884
jl_toplevel_eval_flex at /source/src/toplevel.c:742
jl_eval_toplevel_stmts at /source/src/toplevel.c:585
jl_toplevel_eval_flex at /source/src/toplevel.c:683
ijl_toplevel_eval at /source/src/toplevel.c:754
ijl_toplevel_eval_in at /source/src/toplevel.c:799
eval at ./boot.jl:489
exec_options at ./client.jl:310
_start at ./client.jl:577
jfptr__start_73146.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [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: 0x7aab93fa3249) at /lib/x86_64-linux-gnu/libc.so.6
__libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S
unknown function (ip: (nil)) at (unknown file)


==============================================================
Profile collected. A report will print at the next yield point.
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:1246
wait_forever at ./task.jl:1168
jfptr_wait_forever_58303.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [inlined]
start_task at /source/src/task.c:1272
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/v1.14/Profile/src/Profile.jl:1361
Overhead ╎ [+additional indent] Count File:Line  Function
=========================================================
Thread 1 (default) Task 0x000077975a7f45b0 Total snapshots: 372. Utilization: 0%
   ╎372 @Base/task.jl:1168  wait_forever()
371╎ 372 @Base/task.jl:1246  wait()


Gibbs constructors
[1] signal 15: Terminated
in expression starting at /PkgEval.jl/scripts/evaluate.jl:210
epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404
uv_run at /workspace/srcdir/libuv/src/unix/core.c:430
ijl_task_get_next at /source/src/scheduler.c:457
wait at ./task.jl:1246
wait_forever at ./task.jl:1168
jfptr_wait_forever_58303.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [inlined]
start_task at /source/src/task.c:1272
unknown function (ip: (nil)) at (unknown file)
Allocations: 26605455 (Pool: 26604757; Big: 698); GC: 23

[233] signal 15: Terminated
in expression starting at /home/pkgeval/.julia/packages/Turing/lmRwJ/test/mcmc/gibbs.jl:363
_ZN4llvm12DenseMapBaseINS_13SmallDenseMapISt4pairIPNS_10BasicBlockES4_EiLj4ENS_12DenseMapInfoIS5_vEENS_6detail12DenseMapPairIS5_iEEEES5_iS7_SA_EixEOS5_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZZN4llvm3cfg15LegalizeUpdatesIPNS_10BasicBlockEEEvNS_8ArrayRefINS0_6UpdateIT_EEEERNS_15SmallVectorImplIS7_EEbbENKUlRKNS5_IS3_EESE_E_clESE_SE_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm3cfg15LegalizeUpdatesIPNS_10BasicBlockEEEvNS_8ArrayRefINS0_6UpdateIT_EEEERNS_15SmallVectorImplIS7_EEbb at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm21GenericDomTreeUpdaterINS_14DomTreeUpdaterENS_13DominatorTreeENS_17PostDominatorTreeEE16applyUpdatesImplILb1EEEvv at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm17JumpThreadingPass3runERNS_8FunctionERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91
_ZN4llvm11PassManagerINS_8FunctionENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91
_ZN4llvm27ModuleToFunctionPassAdaptor3runERNS_6ModuleERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91
_ZN4llvm11PassManagerINS_6ModuleENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/src/pipeline.cpp:787
operator() at /source/src/jitlayers.cpp:1508
withModuleDo<(anonymous namespace)::sizedOptimizerT<N>::operator()(llvm::orc::ThreadSafeModule) [with long unsigned int N = 4]::<lambda(llvm::Module&)> > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:136 [inlined]
operator() at /source/src/jitlayers.cpp:1469 [inlined]
operator() at /source/src/jitlayers.cpp:1644 [inlined]
addModule at /source/src/jitlayers.cpp:2101
jl_compile_codeinst_now at /source/src/jitlayers.cpp:682
jl_compile_codeinst_impl at /source/src/jitlayers.cpp:876
jl_compile_method_internal at /source/src/gf.c:3648
_jl_invoke at /source/src/gf.c:4108 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [inlined]
jl_f_invokelatest at /source/src/builtins.c:889
profile_printing_listener at ./Base.jl:337
#start_profile_listener##0 at ./Base.jl:355
jfptr_YY.start_profile_listenerYY.YY.0_15294.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2284 [inlined]
start_task at /source/src/task.c:1272
unknown function (ip: (nil)) at (unknown file)
Allocations: 1150325559 (Pool: 1150318444; Big: 7115); GC: 197

PkgEval terminated after 2723.42s: test duration exceeded the time limit