Package evaluation to test TuringCallbacks on Julia 1.14.0-DEV.1384 (b34261b5d0*) started at 2025-12-18T17:23:33.564


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

Installing PkgEval dependencies (TestEnv)...
  Activating project at `~/.julia/environments/v1.14`

Set-up completed after 9.5s


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

Installing TuringCallbacks...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [ea0860ee] + TuringCallbacks v0.4.4
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [1520ce14] + AbstractTrees v0.4.5
  [66dad0bd] + AliasTables v1.1.3
  [e1450e63] + BufferedStreams v1.2.2
  [3da002f7] + ColorTypes v0.12.1
  [c3611d14] + ColorVectorSpace v0.11.0
  [5ae59095] + Colors v0.13.1
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.19.3
  [31c24e10] + Distributions v0.25.122
  [ffbed154] + DocStringExtensions v0.9.5
  [4e289a0a] + EnumX v1.0.5
  [5789e2e9] + FileIO v1.17.1
  [1a297f60] + FillArrays v1.15.0
  [53c48c17] + FixedPointNumbers v0.8.5
  [34004b35] + HypergeometricFunctions v0.3.28
  [a09fc81d] + ImageCore v0.10.5
  [92d709cd] + IrrationalConstants v0.2.6
  [692b3bcd] + JLLWrappers v1.7.1
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [dbb5928d] + MappedArrays v0.4.3
  [e1d29d7a] + Missings v1.2.0
  [e94cdb99] + MosaicViews v0.3.4
  [6fe1bfb0] + OffsetArrays v1.17.0
  [a15396b6] + OnlineStats v1.7.3
  [925886fa] + OnlineStatsBase v1.7.1
  [bac558e1] + OrderedCollections v1.8.1
  [90014a1f] + PDMats v0.11.36
  [5432bcbf] + PaddedViews v0.5.12
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [3349acd9] + ProtoBuf v1.2.0
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [3cdcf5f2] + RecipesBase v1.3.4
  [189a3867] + Reexport v1.2.2
  [ae029012] + Requires v1.3.1
  [79098fc4] + Rmath v0.9.0
  [a2af1166] + SortingAlgorithms v1.2.2
  [276daf66] + SpecialFunctions v2.6.1
  [cae243ae] + StackViews v0.1.2
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.8.0
  [2913bbd2] + StatsBase v0.34.9
  [4c63d2b9] + StatsFuns v1.5.2
  [899adc3e] + TensorBoardLogger v0.1.26
  [62fd8b95] + TensorCore v0.1.1
  [ea0860ee] + TuringCallbacks v0.4.4
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [0dad84c5] + ArgTools v1.1.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [8bf52ea8] + CRC32c v1.11.0
  [ade2ca70] + Dates v1.11.0
  [f43a241f] + Downloads v1.7.0
  [7b1f6079] + FileWatching v1.11.0
  [ac6e5ff7] + JuliaSyntaxHighlighting v1.13.0
  [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
  [ca575930] + NetworkOptions v1.3.0
  [44cfe95a] + Pkg v1.14.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v1.0.0
  [9e88b42a] + Serialization v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [f489334b] + StyledStrings v1.13.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [deac9b47] + LibCURL_jll v8.17.0+0
  [e37daf67] + LibGit2_jll v1.9.2+0
  [29816b5a] + LibSSH2_jll v1.11.3+1
  [14a3606d] + MozillaCACerts_jll v2025.12.2
  [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

Installation completed after 5.57s


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

ERROR: LoadError: MethodError: no method matching setindex!(::Base.ScopedValues.ScopedValue{IO}, ::Nothing)
The function `setindex!` exists, but no method is defined for this combination of argument types.
Stacktrace:
 [1] top-level scope
   @ /PkgEval.jl/scripts/precompile.jl:10
 [2] include(mod::Module, _path::String)
   @ Base ./Base.jl:309
 [3] exec_options(opts::Base.JLOptions)
   @ Base ./client.jl:344
 [4] _start()
   @ Base ./client.jl:577
in expression starting at /PkgEval.jl/scripts/precompile.jl:6

caused by: MethodError: no method matching setindex!(::Base.ScopedValues.ScopedValue{IO}, ::Base.DevNull)
The function `setindex!` exists, but no method is defined for this combination of argument types.
Stacktrace:
 [1] top-level scope
   @ /PkgEval.jl/scripts/precompile.jl:7
 [2] include(mod::Module, _path::String)
   @ Base ./Base.jl:309
 [3] exec_options(opts::Base.JLOptions)
   @ Base ./client.jl:344
 [4] _start()
   @ Base ./client.jl:577

Precompilation failed after 14.86s



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

     Testing TuringCallbacks
        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_kwWUAm/Project.toml`
⌅ [366bfd00] + DynamicPPL v0.36.15
⌅ [fce5fe82] + Turing v0.39.10
  [ea0860ee] + TuringCallbacks v0.4.4
  [98cad3c8] + ValueHistories v0.5.4
  [8dfed614] ~ Test ⇒ v1.11.0
    Updating `/tmp/jl_kwWUAm/Manifest.toml`
  [47edcb42] + ADTypes v1.20.0
  [621f4979] + AbstractFFTs v1.5.0
  [80f14c24] + AbstractMCMC v5.10.0
⌅ [7a57a42e] + AbstractPPL v0.12.0
  [1520ce14] + AbstractTrees v0.4.5
  [7d9f7c33] + Accessors v0.1.43
  [79e6a3ab] + Adapt v4.4.0
  [0bf59076] + AdvancedHMC v0.8.3
  [5b7e9947] + AdvancedMH v0.8.9
  [576499cb] + AdvancedPS v0.7.2
⌅ [b5ca4192] + AdvancedVI v0.4.1
  [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 v0.15.14
  [082447d4] + ChainRules v1.72.6
  [d360d2e6] + ChainRulesCore v1.26.0
  [0ca39b1e] + Chairmarks v1.3.1
  [9e997f8a] + ChangesOfVariables v0.1.10
  [861a8166] + Combinatorics v1.1.0
  [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
⌅ [864edb3b] ↓ DataStructures v0.19.3 ⇒ v0.18.22
  [e2d170a0] + DataValueInterfaces v1.0.0
  [244e2a9f] + DefineSingletons v0.1.2
  [8bb1440f] + DelimitedFiles v1.9.1
  [b429d917] + DensityInterface v0.4.0
  [163ba53b] + DiffResults v1.1.0
  [b552c78f] + DiffRules v1.15.1
  [a0c0ee7d] + DifferentiationInterface v0.7.12
  [31c24e10] + Distributions v0.25.122
  [ced4e74d] + DistributionsAD v0.6.58
⌅ [366bfd00] + DynamicPPL v0.36.15
  [cad2338a] + EllipticalSliceSampling v2.0.0
  [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 v0.7.13
  [3587e190] + InverseFunctions v0.1.17
  [41ab1584] + InvertedIndices v1.3.1
  [c8e1da08] + IterTools v1.10.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [692b3bcd] + JLLWrappers v1.7.1
⌅ [682c06a0] + JSON v0.21.4
  [ae98c720] + Jieko v0.2.1
  [5ab0869b] + KernelDensity v0.6.10
  [5be7bae1] + LBFGSB v0.4.1
  [b964fa9f] + LaTeXStrings v1.4.0
  [1d6d02ad] + LeftChildRightSiblingTrees v0.2.1
  [6f1fad26] + Libtask v0.9.10
  [d3d80556] + LineSearches v7.5.1
  [6fdf6af0] + LogDensityProblems v2.2.0
  [996a588d] + LogDensityProblemsAD v1.13.1
  [e6f89c97] + LoggingExtras v1.2.0
  [c7f686f2] + MCMCChains v7.6.0
  [be115224] + MCMCDiagnosticTools v0.3.15
  [e80e1ace] + MLJModelInterface v1.12.1
  [1914dd2f] + MacroTools v0.5.16
  [128add7d] + MicroCollections v0.2.0
  [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
  [a15396b6] + OnlineStats v1.7.3
  [925886fa] + OnlineStatsBase v1.7.1
  [429524aa] + Optim v1.13.3
  [3bd65402] + Optimisers v0.4.7
⌅ [7f7a1694] + Optimization v4.8.0
⌅ [bca83a33] + OptimizationBase v2.14.0
⌃ [36348300] + OptimizationOptimJL v0.4.5
  [90014a1f] + PDMats v0.11.36
  [69de0a69] + Parsers v2.8.3
  [85a6dd25] + PositiveFactorizations v0.2.4
  [d236fae5] + PreallocationTools v0.4.34
  [08abe8d2] + PrettyTables v3.1.2
  [33c8b6b6] + ProgressLogging v0.1.6
  [92933f4c] + ProgressMeter v1.11.0
  [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 v3.41.0
  [79098fc4] + Rmath v0.9.0
  [f2b01f46] + Roots v2.2.10
  [7e49a35a] + RuntimeGeneratedFunctions v0.5.16
  [26aad666] + SSMProblems v0.6.1
  [0bca4576] + SciMLBase v2.128.0
  [a6db7da4] + SciMLLogging v1.7.1
  [c0aeaf25] + SciMLOperators v1.14.1
  [431bcebd] + SciMLPublic v1.0.0
  [53ae85a6] + SciMLStructures v1.7.0
  [30f210dd] + ScientificTypesBase v3.0.0
  [efcf1570] + Setfield v1.1.2
  [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
  [4c63d2b9] + StatsFuns v1.5.2
  [892a3eda] + StringManipulation v0.4.2
  [09ab397b] + StructArrays v0.7.2
  [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 v0.39.10
  [ea0860ee] + TuringCallbacks v0.4.4
  [98cad3c8] + ValueHistories v0.5.4
  [efce3f68] + WoodburyMatrices v1.0.0
  [700de1a5] + ZygoteRules v0.2.7
  [f5851436] + FFTW_jll v3.3.11+0
  [1d5cc7b8] + IntelOpenMP_jll v2025.2.0+0
  [81d17ec3] + L_BFGS_B_jll v3.0.1+0
  [856f044c] + MKL_jll v2025.2.0+0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [1317d2d5] + oneTBB_jll v2022.0.0+1
  [8ba89e20] + Distributed v1.11.0
  [9fa8497b] + Future v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [4af54fe1] + LazyArtifacts v1.11.0
  [a63ad114] + Mmap v1.11.0
  [3fa0cd96] + REPL v1.11.0
  [1a1011a3] + SharedArrays v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [4607b0f0] + SuiteSparse
  [8dfed614] ~ Test ⇒ v1.11.0
  [05823500] + OpenLibm_jll v0.8.7+0
        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
        Test Successfully re-resolved
      Status `/tmp/jl_kwWUAm/Project.toml`
⌅ [366bfd00] DynamicPPL v0.36.15
  [899adc3e] TensorBoardLogger v0.1.26
⌅ [fce5fe82] Turing v0.39.10
  [ea0860ee] TuringCallbacks v0.4.4
  [98cad3c8] ValueHistories v0.5.4
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_kwWUAm/Manifest.toml`
  [47edcb42] ADTypes v1.20.0
  [621f4979] AbstractFFTs v1.5.0
  [80f14c24] AbstractMCMC v5.10.0
⌅ [7a57a42e] AbstractPPL v0.12.0
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.43
  [79e6a3ab] Adapt v4.4.0
  [0bf59076] AdvancedHMC v0.8.3
  [5b7e9947] AdvancedMH v0.8.9
  [576499cb] AdvancedPS v0.7.2
⌅ [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 v0.15.14
  [e1450e63] BufferedStreams v1.2.2
  [082447d4] ChainRules v1.72.6
  [d360d2e6] ChainRulesCore v1.26.0
  [0ca39b1e] Chairmarks v1.3.1
  [9e997f8a] ChangesOfVariables v0.1.10
  [3da002f7] ColorTypes v0.12.1
  [c3611d14] ColorVectorSpace v0.11.0
  [5ae59095] Colors v0.13.1
  [861a8166] Combinatorics v1.1.0
  [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 v0.18.22
  [e2d170a0] DataValueInterfaces v1.0.0
  [244e2a9f] DefineSingletons v0.1.2
  [8bb1440f] DelimitedFiles v1.9.1
  [b429d917] DensityInterface v0.4.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [a0c0ee7d] DifferentiationInterface v0.7.12
  [31c24e10] Distributions v0.25.122
  [ced4e74d] DistributionsAD v0.6.58
  [ffbed154] DocStringExtensions v0.9.5
⌅ [366bfd00] DynamicPPL v0.36.15
  [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
  [5789e2e9] FileIO v1.17.1
  [1a297f60] FillArrays v1.15.0
  [6a86dc24] FiniteDiff v2.29.0
  [53c48c17] FixedPointNumbers v0.8.5
  [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
  [a09fc81d] ImageCore v0.10.5
  [22cec73e] InitialValues v0.3.1
  [a98d9a8b] Interpolations v0.16.2
  [8197267c] IntervalSets v0.7.13
  [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 v0.21.4
  [ae98c720] Jieko v0.2.1
  [5ab0869b] KernelDensity v0.6.10
  [5be7bae1] LBFGSB v0.4.1
  [b964fa9f] LaTeXStrings v1.4.0
  [1d6d02ad] LeftChildRightSiblingTrees v0.2.1
  [6f1fad26] Libtask v0.9.10
  [d3d80556] LineSearches v7.5.1
  [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 v1.12.1
  [1914dd2f] MacroTools v0.5.16
  [dbb5928d] MappedArrays v0.4.3
  [128add7d] MicroCollections v0.2.0
  [e1d29d7a] Missings v1.2.0
  [dbe65cb8] MistyClosures v2.1.0
  [e94cdb99] MosaicViews v0.3.4
  [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
  [a15396b6] OnlineStats v1.7.3
  [925886fa] OnlineStatsBase v1.7.1
  [429524aa] Optim v1.13.3
  [3bd65402] Optimisers v0.4.7
⌅ [7f7a1694] Optimization v4.8.0
⌅ [bca83a33] OptimizationBase v2.14.0
⌃ [36348300] OptimizationOptimJL v0.4.5
  [bac558e1] OrderedCollections v1.8.1
  [90014a1f] PDMats v0.11.36
  [5432bcbf] PaddedViews v0.5.12
  [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 v3.1.2
  [33c8b6b6] ProgressLogging v0.1.6
  [92933f4c] ProgressMeter v1.11.0
  [3349acd9] ProtoBuf v1.2.0
  [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 v3.41.0
  [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 v0.6.1
  [0bca4576] SciMLBase v2.128.0
  [a6db7da4] SciMLLogging v1.7.1
  [c0aeaf25] SciMLOperators v1.14.1
  [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
  [cae243ae] StackViews v0.1.2
  [90137ffa] StaticArrays v1.9.15
  [1e83bf80] StaticArraysCore v1.4.4
  [64bff920] StatisticalTraits v3.5.0
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.8.0
  [2913bbd2] StatsBase v0.34.9
  [4c63d2b9] StatsFuns v1.5.2
  [892a3eda] StringManipulation v0.4.2
  [09ab397b] StructArrays v0.7.2
  [2efcf032] SymbolicIndexingInterface v0.3.46
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.12.1
  [899adc3e] TensorBoardLogger v0.1.26
  [62fd8b95] TensorCore v0.1.1
  [5d786b92] TerminalLoggers v0.1.7
  [28d57a85] Transducers v0.4.85
⌅ [fce5fe82] Turing v0.39.10
  [ea0860ee] TuringCallbacks v0.4.4
  [98cad3c8] ValueHistories v0.5.4
  [efce3f68] WoodburyMatrices v1.0.0
  [700de1a5] ZygoteRules v0.2.7
  [f5851436] FFTW_jll v3.3.11+0
  [1d5cc7b8] IntelOpenMP_jll v2025.2.0+0
  [81d17ec3] L_BFGS_B_jll v3.0.1+0
  [856f044c] MKL_jll v2025.2.0+0
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [f50d1b31] Rmath_jll v0.5.1+0
  [1317d2d5] oneTBB_jll v2022.0.0+1
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [8bf52ea8] CRC32c 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 v1.13.0
  [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 v1.14.0
  [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 v1.13.0
  [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 v8.17.0+0
  [e37daf67] LibGit2_jll v1.9.2+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.12.2
  [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 ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading.
     Testing Running tests...
┌ Warning: The call to compilecache failed to create a usable precompiled cache file for TuringCallbacks [ea0860ee-d0ef-45ef-82e6-cc37d6be2f9c]
│   exception = Required dependency Base.PkgId(Base.UUID("5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"), "FileIO") failed to load from a cache file.
└ @ Base loading.jl:2923
┌ Warning: The call to compilecache failed to create a usable precompiled cache file for TensorBoardLogger [899adc3e-224a-11e9-021f-63837185c80f]
│   exception = Required dependency Base.PkgId(Base.UUID("5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"), "FileIO") failed to load from a cache file.
└ @ Base loading.jl:2923
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("899adc3e-224a-11e9-021f-63837185c80f"), "TensorBoardLogger") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=0, inline=true, opt_level=0)
Stacktrace:
  [1] error(s::String)
    @ Base ./error.jl:44
  [2] __require_prelocked(pkg::Base.PkgId, env::String)
    @ Base ./loading.jl:2845
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2697
  [4] macro expansion
    @ ./loading.jl:2625 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2589
  [7] require
    @ ./loading.jl:2565 [inlined]
  [8] eval_import_path
    @ ./module.jl:36 [inlined]
  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)
    @ Base ./module.jl:60
 [10] _eval_using
    @ ./module.jl:137 [inlined]
 [11] _eval_using(to::Module, path::Expr)
    @ Base ./module.jl:137
 [12] top-level scope
    @ ~/.julia/packages/TuringCallbacks/rZplz/src/TuringCallbacks.jl:11
 [13] include(mod::Module, _path::String)
    @ Base ./Base.jl:309
 [14] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)
    @ Base ./loading.jl:3282
 [15] top-level scope
    @ stdin:5
 [16] eval(m::Module, e::Any)
    @ Core ./boot.jl:489
 [17] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)
    @ Base ./loading.jl:3124
 [18] include_string
    @ ./loading.jl:3134 [inlined]
 [19] exec_options(opts::Base.JLOptions)
    @ Base ./client.jl:342
 [20] _start()
    @ Base ./client.jl:577
in expression starting at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/src/TuringCallbacks.jl:1
in expression starting at stdin:5
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("ea0860ee-d0ef-45ef-82e6-cc37d6be2f9c"), "TuringCallbacks") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=0, inline=true, opt_level=0)
Stacktrace:
  [1] error(s::String)
    @ Base ./error.jl:44
  [2] __require_prelocked(pkg::Base.PkgId, env::String)
    @ Base ./loading.jl:2845
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2697
  [4] macro expansion
    @ ./loading.jl:2625 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2589
  [7] require
    @ ./loading.jl:2565 [inlined]
  [8] eval_import_path
    @ ./module.jl:36 [inlined]
  [9] eval_import_path_all(at::Module, path::Expr, keyword::String)
    @ Base ./module.jl:60
 [10] _eval_import(::Bool, ::Module, ::Expr, ::Expr, ::Vararg{Expr})
    @ Base ./module.jl:101
 [11] top-level scope
    @ ~/.julia/packages/TuringCallbacks/rZplz/ext/TuringCallbacksTuringExt.jl:5
 [12] include(mod::Module, _path::String)
    @ Base ./Base.jl:309
 [13] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)
    @ Base ./loading.jl:3282
 [14] top-level scope
    @ stdin:5
 [15] eval(m::Module, e::Any)
    @ Core ./boot.jl:489
 [16] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)
    @ Base ./loading.jl:3124
 [17] include_string
    @ ./loading.jl:3134 [inlined]
 [18] exec_options(opts::Base.JLOptions)
    @ Base ./client.jl:342
 [19] _start()
    @ Base ./client.jl:577
in expression starting at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/ext/TuringCallbacksTuringExt.jl:1
in expression starting at stdin:5

  2 dependencies had output during precompilation:
┌ TuringCallbacks
│  ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("899adc3e-224a-11e9-021f-63837185c80f"), "TensorBoardLogger") not available with flags CacheFlags(; use_pkgimages=false, debug_level=1, check_bounds=0, inline=true, opt_level=0)
│  Stacktrace:
│    [1] error(s::String)
│      @ Base ./error.jl:44
│    [2] __require_prelocked(pkg::Base.PkgId, env::String)
│      @ Base ./loading.jl:2845
│    [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
│      @ Base ./loading.jl:2697
│    [4] macro expansion
│      @ ./loading.jl:2625 [inlined]
│    [5] macro expansion
│      @ ./lock.jl:376 [inlined]
│    [6] __require(into::Module, mod::Symbol)
│      @ Base ./loading.jl:2589
│    [7] require
│      @ ./loading.jl:2565 [inlined]
│    [8] eval_import_path
│      @ ./module.jl:36 [inlined]
│    [9] eval_import_path_all(at::Module, path::Expr, keyword::String)
│      @ Base ./module.jl:60
│   [10] _eval_using
│      @ ./module.jl:137 [inlined]
│   [11] _eval_using(to::Module, path::Expr)
│      @ Base ./module.jl:137
│   [12] top-level scope
│      @ ~/.julia/packages/TuringCallbacks/rZplz/src/TuringCallbacks.jl:11
│   [13] include(mod::Module, _path::String)
│      @ Base ./Base.jl:309
│   [14] include_package_for_output(pkg::Base.PkgId, input::String, syntax_version::VersionNumber, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt128}}, source::Nothing)
│      @ Base ./loading.jl:3282
│   [15] top-level scope
│      @ stdin:5
│   [16] eval(m::Module, e::Any)
│      @ Core ./boot.jl:489
│   [17] include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String)
│      @ Base ./loading.jl:3124
│   [18] include_string
│      @ ./loading.jl:3134 [inlined]
│   [19] exec_options(opts::Base.JLOptions)
│      @ Base ./client.jl:342
│   [20] _start()
│      @ Base ./client.jl:577
│  in expression starting at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/src/TuringCallbacks.jl:1
│  in expression starting at stdin:5
└  
┌ TuringCallbacks → TuringCallbacksTuringExt
│  [Output was shown above]
└  
┌ Error: Error during loading of extension TuringCallbacksTuringExt of TuringCallbacks, use `Base.retry_load_extensions()` to retry.
│   exception =
│    1-element ExceptionStack:
│    The following 2 packages failed to precompile:
│    
│    TuringCallbacks 
│    Failed to precompile TuringCallbacks [ea0860ee-d0ef-45ef-82e6-cc37d6be2f9c] to "/home/pkgeval/.julia/compiled/v1.14/TuringCallbacks/jl_y02lgE" (ProcessExited(1)).
│    
│    TuringCallbacksTuringExt 
│    Failed to precompile TuringCallbacksTuringExt [c1499ae1-b666-5196-bf03-41224ecbb5ec] to "/home/pkgeval/.julia/compiled/v1.14/TuringCallbacksTuringExt/jl_uTyGTY" (ProcessExited(1)).
│    
└ @ Base loading.jl:1753
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┌ Info: Found initial step size
└   ϵ = 0.4
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Sampling   0%|                                          |  ETA: N/A
┌ Info: Found initial step size
└   ϵ = 0.4
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Correctness of values: Error During Test at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:14
  Got exception outside of a @test
  MethodError: no method matching params_and_values(::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, ::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}; nadapts::Int64)
  The function `params_and_values` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    params_and_values(::Any, ::Any, !Matched::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    params_and_values(::Any, ::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
  Stacktrace:
    [1] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:183
    [2] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
    [3] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:186
    [4] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    [5] (::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}})()
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:284
    [6] with_logstate(f::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
    [7] with_logger(f::Function, logger::TBLogger{String, IOStream})
      @ Base.CoreLogging ./logging/logging.jl:653
    [8] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:283
    [9] kwcall(::@NamedTuple{nadapts::Int64}, cb::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64)
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:259
   [10] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:219 [inlined]
   [11] (::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [12] with_logstate(f::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
   [13] 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:653
   [14] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [15] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [16] mcmcsample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nadapts::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [17] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nadapts::Int64, discard_initial::Int64, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [18] sample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, nadapts::Int64, discard_adapt::Bool, discard_initial::Int64, kwargs::@Kwargs{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}})
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:117
   [19] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:86 [inlined]
   [20] #sample#106
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:29 [inlined]
   [21] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:20 [inlined]
   [22] #sample#105
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:17 [inlined]
   [23] kwcall(::@NamedTuple{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(sample), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, alg::NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:14
   [24] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:19 [inlined]
   [25] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [26] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:16 [inlined]
   [27] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [28] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:2
   [29] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [30] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:28
   [31] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [32] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:29 [inlined]
   [33] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [34] top-level scope
      @ none:6
   [35] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [36] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [37] _start()
      @ Base ./client.jl:577
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┌ Info: Found initial step size
└   ϵ = 0.4
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Default: Error During Test at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:32
  Got exception outside of a @test
  MethodError: no method matching params_and_values(::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, ::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}; nadapts::Int64)
  The function `params_and_values` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    params_and_values(::Any, ::Any, !Matched::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    params_and_values(::Any, ::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
  Stacktrace:
    [1] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:183
    [2] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
    [3] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:186
    [4] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    [5] (::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}})()
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:284
    [6] with_logstate(f::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
    [7] with_logger(f::Function, logger::TBLogger{String, IOStream})
      @ Base.CoreLogging ./logging/logging.jl:653
    [8] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:283
    [9] kwcall(::@NamedTuple{nadapts::Int64}, cb::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64)
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:259
   [10] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:219 [inlined]
   [11] (::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [12] with_logstate(f::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
   [13] 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:653
   [14] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [15] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [16] mcmcsample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nadapts::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [17] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nadapts::Int64, discard_initial::Int64, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [18] sample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, nadapts::Int64, discard_adapt::Bool, discard_initial::Int64, kwargs::@Kwargs{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}})
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:117
   [19] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:86 [inlined]
   [20] #sample#106
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:29 [inlined]
   [21] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:20 [inlined]
   [22] #sample#105
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:17 [inlined]
   [23] kwcall(::@NamedTuple{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(sample), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, alg::NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:14
   [24] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:39 [inlined]
   [25] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [26] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:34 [inlined]
   [27] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [28] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:2
   [29] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [30] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:28
   [31] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [32] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:29 [inlined]
   [33] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [34] top-level scope
      @ none:6
   [35] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [36] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [37] _start()
      @ Base ./client.jl:577
Sampling   0%|                                          |  ETA: N/A
┌ Info: Found initial step size
└   ϵ = 0.4
Sampling   1%|▎                                         |  ETA: 0:00:01
Sampling   1%|▌                                         |  ETA: 0:00:00
Sampling   2%|▉                                         |  ETA: 0:00:00
Sampling   3%|█▏                                        |  ETA: 0:00:00
Sampling   3%|█▍                                        |  ETA: 0:00:00
Sampling   4%|█▋                                        |  ETA: 0:00:00
Sampling   5%|██                                        |  ETA: 0:00:00
Sampling   5%|██▎                                       |  ETA: 0:00:00
Sampling   6%|██▌                                       |  ETA: 0:00:00
Sampling   7%|██▊                                       |  ETA: 0:00:00
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Exclude variable: Error During Test at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:60
  Got exception outside of a @test
  MethodError: no method matching params_and_values(::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, ::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}; nadapts::Int64)
  The function `params_and_values` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    params_and_values(::Any, ::Any, !Matched::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    params_and_values(::Any, ::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
  Stacktrace:
    [1] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:183
    [2] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
    [3] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:186
    [4] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    [5] (::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}})()
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:284
    [6] with_logstate(f::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
    [7] with_logger(f::Function, logger::TBLogger{String, IOStream})
      @ Base.CoreLogging ./logging/logging.jl:653
    [8] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:283
    [9] kwcall(::@NamedTuple{nadapts::Int64}, cb::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64)
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:259
   [10] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:219 [inlined]
   [11] (::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [12] with_logstate(f::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
   [13] 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:653
   [14] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [15] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [16] mcmcsample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nadapts::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [17] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nadapts::Int64, discard_initial::Int64, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [18] sample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, nadapts::Int64, discard_adapt::Bool, discard_initial::Int64, kwargs::@Kwargs{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}})
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:117
   [19] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:86 [inlined]
   [20] #sample#106
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:29 [inlined]
   [21] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:20 [inlined]
   [22] #sample#105
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:17 [inlined]
   [23] kwcall(::@NamedTuple{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Vector{String}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(sample), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, alg::NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:14
   [24] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:68 [inlined]
   [25] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [26] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:62 [inlined]
   [27] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [28] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:2
   [29] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [30] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:28
   [31] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [32] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:29 [inlined]
   [33] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [34] top-level scope
      @ none:6
   [35] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [36] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [37] _start()
      @ Base ./client.jl:577
Sampling   0%|                                          |  ETA: N/A
┌ Info: Found initial step size
└   ϵ = 0.4
Sampling   1%|▎                                         |  ETA: 0:00:00
Sampling   1%|▌                                         |  ETA: 0:00:00
Sampling   2%|▉                                         |  ETA: 0:00:00
Sampling   3%|█▏                                        |  ETA: 0:00:00
Sampling   3%|█▍                                        |  ETA: 0:00:00
Sampling   4%|█▋                                        |  ETA: 0:00:00
Sampling   5%|██                                        |  ETA: 0:00:00
Sampling   5%|██▎                                       |  ETA: 0:00:00
Sampling   6%|██▌                                       |  ETA: 0:00:00
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Exclude extras: Error During Test at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:93
  Got exception outside of a @test
  MethodError: no method matching params_and_values(::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, ::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}; nadapts::Int64)
  The function `params_and_values` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    params_and_values(::Any, ::Any, !Matched::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    params_and_values(::Any, ::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
  Stacktrace:
    [1] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:183
    [2] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
    [3] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:186
    [4] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    [5] (::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}})()
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:284
    [6] with_logstate(f::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
    [7] with_logger(f::Function, logger::TBLogger{String, IOStream})
      @ Base.CoreLogging ./logging/logging.jl:653
    [8] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:283
    [9] kwcall(::@NamedTuple{nadapts::Int64}, cb::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64)
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:259
   [10] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:219 [inlined]
   [11] (::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [12] with_logstate(f::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
   [13] 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:653
   [14] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [15] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [16] mcmcsample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nadapts::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [17] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nadapts::Int64, discard_initial::Int64, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [18] sample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, nadapts::Int64, discard_adapt::Bool, discard_initial::Int64, kwargs::@Kwargs{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}})
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:117
   [19] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:86 [inlined]
   [20] #sample#106
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:29 [inlined]
   [21] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:20 [inlined]
   [22] #sample#105
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:17 [inlined]
   [23] kwcall(::@NamedTuple{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(sample), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, alg::NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:14
   [24] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:101 [inlined]
   [25] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [26] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:95 [inlined]
   [27] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [28] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:2
   [29] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [30] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:28
   [31] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [32] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:29 [inlined]
   [33] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [34] top-level scope
      @ none:6
   [35] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [36] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [37] _start()
      @ Base ./client.jl:577
Sampling   0%|                                          |  ETA: N/A
Sampling   1%|▍                                         |  ETA: 0:05:16
┌ Warning: `hyperparams(model, sampler; kwargs...)` is not implemented for Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext} and Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}. If you want to record hyperparameters, please implement this method.
└ @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:221
Sampling 100%|██████████████████████████████████████████| Time: 0:00:03

HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}(0.05, 10, AutoForwardDiff()) (has hyperparam: true): Error During Test at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121
  Got exception outside of a @test
  MethodError: no method matching params_and_values(::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, ::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}})
  The function `params_and_values` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    params_and_values(::Any, ::Any, !Matched::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    params_and_values(::Any, ::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
  Stacktrace:
    [1] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}; kwargs::@Kwargs{})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:183
    [2] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
    [3] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}; kwargs::@Kwargs{})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:186
    [4] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    [5] (::TuringCallbacks.var"#27#28"{@Kwargs{}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}})()
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:284
    [6] with_logstate(f::TuringCallbacks.var"#27#28"{@Kwargs{}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
    [7] with_logger(f::Function, logger::TBLogger{String, IOStream})
      @ Base.CoreLogging ./logging/logging.jl:653
    [8] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, iteration::Int64; kwargs::@Kwargs{})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:283
    [9] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedNSteps}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.NoAdaptation}, iteration::Int64)
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:259
   [10] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:219 [inlined]
   [11] (::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [12] with_logstate(f::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
   [13] 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:653
   [14] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [15] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [16] mcmcsample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [17] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [18] sample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/nxcz4/src/sampler.jl:102
   [19] sample
      @ ~/.julia/packages/DynamicPPL/nxcz4/src/sampler.jl:92 [inlined]
   [20] #sample#106
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:29 [inlined]
   [21] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:20 [inlined]
   [22] #sample#105
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:17 [inlined]
   [23] kwcall(::@NamedTuple{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(sample), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, alg::HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:14
   [24] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:135 [inlined]
   [25] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2042 [inlined]
   [26] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121 [inlined]
   [27] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [28] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121 [inlined]
   [29] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [30] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:2
   [31] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [32] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:28
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:29 [inlined]
   [35] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [36] top-level scope
      @ none:6
   [37] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [38] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [39] _start()
      @ Base ./client.jl:577
Sampling   0%|                                          |  ETA: N/A
┌ Info: Found initial step size
└   ϵ = 0.4
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┌ Warning: `hyperparams(model, sampler; kwargs...)` is not implemented for Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext} and Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}. If you want to record hyperparameters, please implement this method.
└ @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:221
Sampling 100%|██████████████████████████████████████████| Time: 0:00:04

HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}(50, 0.65, 1.0, 0.0, AutoForwardDiff()) (has hyperparam: true): Error During Test at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121
  Got exception outside of a @test
  MethodError: no method matching params_and_values(::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, ::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}; nadapts::Int64)
  The function `params_and_values` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    params_and_values(::Any, ::Any, !Matched::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    params_and_values(::Any, ::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
  Stacktrace:
    [1] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedIntegrationTime{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.UnitMassMatrix{Float64}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:183
    [2] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedIntegrationTime{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.UnitMassMatrix{Float64}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
    [3] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedIntegrationTime{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.UnitMassMatrix{Float64}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:186
    [4] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedIntegrationTime{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.UnitMassMatrix{Float64}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    [5] (::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedIntegrationTime{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.UnitMassMatrix{Float64}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}})()
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:284
    [6] with_logstate(f::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedIntegrationTime{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.UnitMassMatrix{Float64}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
    [7] with_logger(f::Function, logger::TBLogger{String, IOStream})
      @ Base.CoreLogging ./logging/logging.jl:653
    [8] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedIntegrationTime{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.UnitMassMatrix{Float64}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:283
    [9] kwcall(::@NamedTuple{nadapts::Int64}, cb::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.EndPointTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.FixedIntegrationTime{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.UnitEuclideanMetric{Float64, Tuple{Int64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.UnitMassMatrix{Float64}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64)
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:259
   [10] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:219 [inlined]
   [11] (::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [12] with_logstate(f::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
   [13] 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:653
   [14] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [15] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [16] mcmcsample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nadapts::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [17] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nadapts::Int64, discard_initial::Int64, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [18] sample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, nadapts::Int64, discard_adapt::Bool, discard_initial::Int64, kwargs::@Kwargs{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}})
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:117
   [19] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:86 [inlined]
   [20] #sample#106
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:29 [inlined]
   [21] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:20 [inlined]
   [22] #sample#105
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:17 [inlined]
   [23] kwcall(::@NamedTuple{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(sample), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, alg::HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:14
   [24] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:135 [inlined]
   [25] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2042 [inlined]
   [26] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121 [inlined]
   [27] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [28] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121 [inlined]
   [29] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [30] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:2
   [31] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [32] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:28
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:29 [inlined]
   [35] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [36] top-level scope
      @ none:6
   [37] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [38] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [39] _start()
      @ Base ./client.jl:577
Sampling   0%|                                          |  ETA: N/A
┌ Info: Found initial step size
└   ϵ = 0.4
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┌ Warning: `hyperparams(model, sampler; kwargs...)` is not implemented for Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext} and Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}. If you want to record hyperparameters, please implement this method.
└ @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:221
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00

NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}(50, 0.65, 10, 1000.0, 0.0, AutoForwardDiff()) (has hyperparam: true): Error During Test at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121
  Got exception outside of a @test
  MethodError: no method matching params_and_values(::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, ::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}; nadapts::Int64)
  The function `params_and_values` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    params_and_values(::Any, ::Any, !Matched::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    params_and_values(::Any, ::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
  Stacktrace:
    [1] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:183
    [2] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
    [3] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:186
    [4] kwcall(::@NamedTuple{nadapts::Int64}, ::typeof(TuringCallbacks.params_and_values), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    [5] (::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}})()
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:284
    [6] with_logstate(f::TuringCallbacks.var"#27#28"{@Kwargs{nadapts::Int64}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
    [7] with_logger(f::Function, logger::TBLogger{String, IOStream})
      @ Base.CoreLogging ./logging/logging.jl:653
    [8] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64; kwargs::@Kwargs{nadapts::Int64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:283
    [9] kwcall(::@NamedTuple{nadapts::Int64}, cb::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, @NamedTuple{n_steps::Int64, is_accept::Bool, acceptance_rate::Float64, log_density::Float64, hamiltonian_energy::Float64, hamiltonian_energy_error::Float64, max_hamiltonian_energy_error::Float64, tree_depth::Int64, numerical_error::Bool, step_size::Float64, nom_step_size::Float64}}, state::Turing.Inference.HMCState{VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, AdvancedHMC.HMCKernel{AdvancedHMC.FullMomentumRefreshment, AdvancedHMC.Trajectory{AdvancedHMC.MultinomialTS, AdvancedHMC.Leapfrog{Float64}, AdvancedHMC.GeneralisedNoUTurn{Float64}}}, AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}, Base.Fix1{typeof(LogDensityProblems.logdensity_and_gradient), LogDensityFunction{Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, SamplingContext{Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, DefaultContext, Random.TaskLocalRNG}, AutoForwardDiff{2, ForwardDiff.Tag{DynamicPPL.DynamicPPLTag, Float64}}}}}, AdvancedHMC.PhasePoint{Vector{Float64}, AdvancedHMC.DualValue{Float64, Vector{Float64}}}, AdvancedHMC.Adaptation.StanHMCAdaptor{AdvancedHMC.Adaptation.WelfordVar{Float64, Vector{Float64}, Vector{Float64}}, AdvancedHMC.Adaptation.NesterovDualAveraging{Float64, Float64}}}, iteration::Int64)
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:259
   [10] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:219 [inlined]
   [11] (::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [12] with_logstate(f::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{nadapts::Int64}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
   [13] 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:653
   [14] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [15] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [16] mcmcsample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{nadapts::Int64})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [17] kwcall(::@NamedTuple{chain_type::UnionAll, progress::Bool, nadapts::Int64, discard_initial::Int64, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [18] sample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, progress::Bool, nadapts::Int64, discard_adapt::Bool, discard_initial::Int64, kwargs::@Kwargs{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}})
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:117
   [19] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/hmc.jl:86 [inlined]
   [20] #sample#106
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:29 [inlined]
   [21] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:20 [inlined]
   [22] #sample#105
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:17 [inlined]
   [23] kwcall(::@NamedTuple{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(sample), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, alg::NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:14
   [24] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:135 [inlined]
   [25] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2042 [inlined]
   [26] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121 [inlined]
   [27] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [28] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121 [inlined]
   [29] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [30] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:2
   [31] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [32] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:28
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:29 [inlined]
   [35] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [36] top-level scope
      @ none:6
   [37] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [38] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [39] _start()
      @ Base ./client.jl:577
Sampling   0%|                                          |  ETA: N/A
Sampling   1%|▍                                         |  ETA: 0:00:08
┌ Warning: `hyperparams(model, sampler; kwargs...)` is not implemented for Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext} and Sampler{MH{@NamedTuple{}}}. If you want to record hyperparameters, please implement this method.
└ @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:221
Sampling 100%|██████████████████████████████████████████| Time: 0:00:00

MH{@NamedTuple{}}(NamedTuple()) (has hyperparam: false): Error During Test at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121
  Got exception outside of a @test
  MethodError: no method matching params_and_values(::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, ::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing})
  The function `params_and_values` exists, but no method is defined for this combination of argument types.
  Closest candidates are:
    params_and_values(::Any, ::Any, !Matched::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    params_and_values(::Any, ::Any, !Matched::Any; kwargs...)
     @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
  Stacktrace:
    [1] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing}, state::VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}; kwargs::@Kwargs{})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:183
    [2] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing}, state::VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:182
    [3] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{MH{@NamedTuple{}}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing}, state::VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}; kwargs::@Kwargs{})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:186
    [4] params_and_values(model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{MH{@NamedTuple{}}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing}, state::VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:185
    [5] (::TuringCallbacks.var"#27#28"{@Kwargs{}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{MH{@NamedTuple{}}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}})()
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:284
    [6] with_logstate(f::TuringCallbacks.var"#27#28"{@Kwargs{}, TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{MH{@NamedTuple{}}}, Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing}, VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, Base.Fix1{typeof(TuringCallbacks.filter_extras_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, Base.Fix1{typeof(TuringCallbacks.filter_param_and_value), TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, TBLogger{String, IOStream}, DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
    [7] with_logger(f::Function, logger::TBLogger{String, IOStream})
      @ Base.CoreLogging ./logging/logging.jl:653
    [8] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{MH{@NamedTuple{}}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing}, state::VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, iteration::Int64; kwargs::@Kwargs{})
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:283
    [9] (::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}})(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{MH{@NamedTuple{}}}, transition::Turing.Inference.Transition{OrderedDict{Any, Any}, Float64, Nothing}, state::VarInfo{@NamedTuple{s::DynamicPPL.Metadata{Dict{VarName{:s, typeof(identity)}, Int64}, Vector{InverseGamma{Float64}}, Vector{VarName{:s, typeof(identity)}}, Vector{Float64}}, m::DynamicPPL.Metadata{Dict{VarName{:m, typeof(identity)}, Int64}, Vector{Normal{Float64}}, Vector{VarName{:m, typeof(identity)}}, Vector{Float64}}}, Float64}, iteration::Int64)
      @ TuringCallbacks ~/.julia/packages/TuringCallbacks/rZplz/src/callbacks/tensorboard.jl:259
   [10] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:219 [inlined]
   [11] (::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{MH{@NamedTuple{}}}, Int64, Float64, Int64, Int64})()
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:134
   [12] with_logstate(f::AbstractMCMC.var"#27#28"{TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, Int64, Int64, Int64, Type{Chains}, Nothing, @Kwargs{}, Random.TaskLocalRNG, Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, Sampler{MH{@NamedTuple{}}}, Int64, Float64, Int64, Int64}, logstate::Base.CoreLogging.LogState)
      @ Base.CoreLogging ./logging/logging.jl:542
   [13] 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:653
   [14] with_progresslogger(f::Function, _module::Module, logger::Base.CoreLogging.ConsoleLogger)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:157
   [15] macro expansion
      @ ~/.julia/packages/AbstractMCMC/mcqES/src/logging.jl:133 [inlined]
   [16] mcmcsample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{MH{@NamedTuple{}}}, N::Int64; progress::Bool, progressname::String, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}, num_warmup::Int64, discard_initial::Int64, thinning::Int64, chain_type::Type, initial_state::Nothing, kwargs::@Kwargs{})
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:168
   [17] kwcall(::@NamedTuple{chain_type::UnionAll, initial_state::Nothing, callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(AbstractMCMC.mcmcsample), rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{MH{@NamedTuple{}}}, N::Int64)
      @ AbstractMCMC ~/.julia/packages/AbstractMCMC/mcqES/src/sample.jl:123
   [18] sample(rng::Random.TaskLocalRNG, model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, sampler::Sampler{MH{@NamedTuple{}}}, N::Int64; chain_type::Type, resume_from::Nothing, initial_state::Nothing, kwargs::@Kwargs{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}})
      @ DynamicPPL ~/.julia/packages/DynamicPPL/nxcz4/src/sampler.jl:102
   [19] sample
      @ ~/.julia/packages/DynamicPPL/nxcz4/src/sampler.jl:92 [inlined]
   [20] #sample#106
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:29 [inlined]
   [21] sample
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:20 [inlined]
   [22] #sample#105
      @ ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:17 [inlined]
   [23] kwcall(::@NamedTuple{callback::TensorBoardCallback{DefaultDict{String, Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}, TuringCallbacks.var"#13#14"{Series{Number, Tuple{Mean{Float64, EqualWeight}, Variance{Float64, Float64, EqualWeight}, KHist{Float64}}}}}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}, TuringCallbacks.NameFilter{Nothing, Nothing}}}, ::typeof(sample), model::Model{typeof(demo), (:x,), (), (), Tuple{Vector{Float64}}, Tuple{}, DefaultContext}, alg::MH{@NamedTuple{}}, N::Int64)
      @ Turing.Inference ~/.julia/packages/Turing/EEhvQ/src/mcmc/abstractmcmc.jl:14
   [24] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:135 [inlined]
   [25] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:2042 [inlined]
   [26] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121 [inlined]
   [27] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [28] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:121 [inlined]
   [29] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [30] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/tensorboardcallback.jl:2
   [31] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [32] top-level scope
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:28
   [33] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [34] macro expansion
      @ ~/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:29 [inlined]
   [35] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [36] top-level scope
      @ none:6
   [37] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [38] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [39] _start()
      @ Base ./client.jl:577
Test Summary:                                                                                                                                       | Pass  Error  Total     Time
TuringCallbacks.jl                                                                                                                                  |    3      8     11  1m40.3s
  MultiCallback                                                                                                                                     |    3             3    58.6s
  TensorBoardCallback                                                                                                                               |           8      8    35.3s
    Correctness of values                                                                                                                           |           1      1    13.9s
    Default                                                                                                                                         |           1      1     0.9s
    Exclude variable                                                                                                                                |           1      1     1.8s
    Exclude extras                                                                                                                                  |           1      1     0.9s
    With hyperparams                                                                                                                                |           4      4    17.6s
      HMC{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}(0.05, 10, AutoForwardDiff()) (has hyperparam: true)                   |           1      1     6.8s
      HMCDA{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.UnitEuclideanMetric}(50, 0.65, 1.0, 0.0, AutoForwardDiff()) (has hyperparam: true)       |           1      1     7.0s
      NUTS{AutoForwardDiff{nothing, Nothing}, AdvancedHMC.DiagEuclideanMetric}(50, 0.65, 10, 1000.0, 0.0, AutoForwardDiff()) (has hyperparam: true) |           1      1     0.8s
      MH{@NamedTuple{}}(NamedTuple()) (has hyperparam: false)                                                                                       |           1      1     2.3s
RNG of the outermost testset: Random.Xoshiro(0xdcb9d75c09c94204, 0x5c701a6484c98c34, 0x53b627f22f333331, 0x612561ae022dd236, 0x3c9e9a069366a60f)
ERROR: LoadError: Some tests did not pass: 3 passed, 0 failed, 8 errored, 0 broken.
in expression starting at /home/pkgeval/.julia/packages/TuringCallbacks/rZplz/test/runtests.jl:27

Testing failed after 701.34s

ERROR: LoadError: Package TuringCallbacks errored during testing
Stacktrace:
  [1] pkgerror(msg::String)
    @ Pkg.Types /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Types.jl:68
  [2] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool)
    @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:3067
  [3] test
    @ /opt/julia/share/julia/stdlib/v1.14/Pkg/src/Operations.jl:2916 [inlined]
  [4] test(ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:572
  [5] kwcall(::@NamedTuple{julia_args::Cmd, io::IOContext{IO}}, ::typeof(Pkg.API.test), ctx::Pkg.Types.Context, pkgs::Vector{PackageSpec})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:548
  [6] test(pkgs::Vector{PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:172
  [7] kwcall(::@NamedTuple{julia_args::Cmd}, ::typeof(Pkg.API.test), pkgs::Vector{PackageSpec})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:161
  [8] test(pkgs::Vector{String}; kwargs::@Kwargs{julia_args::Cmd})
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160
  [9] test
    @ /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:160 [inlined]
 [10] kwcall(::@NamedTuple{julia_args::Cmd}, ::typeof(Pkg.API.test), pkg::String)
    @ Pkg.API /opt/julia/share/julia/stdlib/v1.14/Pkg/src/API.jl:159
 [11] top-level scope
    @ /PkgEval.jl/scripts/evaluate.jl:237
 [12] include(mod::Module, _path::String)
    @ Base ./Base.jl:309
 [13] exec_options(opts::Base.JLOptions)
    @ Base ./client.jl:344
 [14] _start()
    @ Base ./client.jl:577
in expression starting at /PkgEval.jl/scripts/evaluate.jl:228

PkgEval failed after 800.86s: package fails to precompile