Package evaluation to test Finch on Julia 1.14.0-DEV.1372 (893635dc59*) started at 2025-12-16T16:32:21.623


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

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

Set-up completed after 9.54s


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

Installing Finch...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [9177782c] + Finch v1.2.12
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [1520ce14] + AbstractTrees v0.4.5
  [66dad0bd] + AliasTables v1.1.3
  [15f4f7f2] + AutoHashEquals v2.2.0
  [34da2185] + Compat v4.18.1
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.19.3
  [31c24e10] + Distributions v0.25.122
  [ffbed154] + DocStringExtensions v0.9.5
  [1a297f60] + FillArrays v1.15.0
  [9177782c] + Finch v1.2.12
  [34004b35] + HypergeometricFunctions v0.3.28
  [92d709cd] + IrrationalConstants v0.2.6
  [c8e1da08] + IterTools v1.10.0
  [692b3bcd] + JLLWrappers v1.7.1
  [682c06a0] + JSON v1.3.0
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [e1d29d7a] + Missings v1.2.0
  [bac558e1] + OrderedCollections v1.8.1
  [90014a1f] + PDMats v0.11.36
  [69de0a69] + Parsers v2.8.3
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.0
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [189a3867] + Reexport v1.2.2
  [ae029012] + Requires v1.3.1
  [5969e224] + RewriteTools v0.2.4
  [79098fc4] + Rmath v0.9.0
  [a2af1166] + SortingAlgorithms v1.2.2
  [276daf66] + SpecialFunctions v2.6.1
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.8.0
  [2913bbd2] + StatsBase v0.34.9
  [4c63d2b9] + StatsFuns v1.5.2
  [ec057cc2] + StructUtils v2.6.0
  [b33eeca9] + SyntaxInterface v0.2.1
  [013be700] + UnsafeAtomics v0.3.0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [56f22d72] + Artifacts v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [56ddb016] + Logging v1.11.0
  [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
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [4536629a] + OpenBLAS_jll v0.3.29+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [8e850b90] + libblastrampoline_jll v5.15.0+0

Installation completed after 4.75s


################################################################################
# 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 13.13s



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

     Testing Finch
        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_nMFQ9D/Project.toml`
  [c7e460c6] + ArgParse v1.2.0
  [6e4b80f9] + BenchmarkTools v1.6.3
  [5a98b6c4] + CIndices v0.3.0
⌅ [864edb3b] ↓ DataStructures v0.19.3 ⇒ v0.18.22
  [e30172f5] + Documenter v1.16.1
⌃ [86223c79] + Graphs v1.13.1
  [f67ccb44] + HDF5 v0.17.2
⌅ [98e50ef6] + JuliaFormatter v1.0.62
  [b51810bb] + MatrixDepot v1.0.13
  [4d4711f2] + MatrixMarket v0.5.2
  [15e1cf62] + NPZ v0.4.3
  [817f1d60] + ReTestItems v1.35.0
  [47aef6b3] + SimpleWeightedGraphs v1.5.1
  [860ef19b] + StableRNGs v1.0.4
  [8b7d4fe7] + TensorMarket v0.2.2
  [7b1f6079] ~ FileWatching ⇒ v1.11.0
  [44cfe95a] ~ Pkg ⇒ v1.14.0
  [8dfed614] ~ Test ⇒ v1.11.0
    Updating `/tmp/jl_nMFQ9D/Manifest.toml`
  [a4c015fc] + ANSIColoredPrinters v0.0.1
  [c7e460c6] + ArgParse v1.2.0
  [ec485272] + ArnoldiMethod v0.4.0
  [6e4b80f9] + BenchmarkTools v1.6.3
  [e1450e63] + BufferedStreams v1.2.2
  [5a98b6c4] + CIndices v0.3.0
  [00ebfdb7] + CSTParser v3.4.3
  [79a69506] + ChannelBuffers v0.4.2
  [944b1d66] + CodecZlib v0.7.8
⌅ [a80b9123] + CommonMark v0.8.16
  [a8cc5b0e] + Crayons v4.1.1
  [a93c6f00] + DataFrames v1.8.1
⌅ [864edb3b] ↓ DataStructures v0.19.3 ⇒ v0.18.22
  [e2d170a0] + DataValueInterfaces v1.0.0
  [e30172f5] + Documenter v1.16.1
  [5789e2e9] + FileIO v1.17.1
  [d7ba0133] + Git v1.5.0
  [c27321d9] + Glob v1.4.0
⌃ [86223c79] + Graphs v1.13.1
  [f67ccb44] + HDF5 v0.17.2
  [b5f81e59] + IOCapture v1.0.0
  [d25df0c9] + Inflate v0.1.5
  [842dd82b] + InlineStrings v1.4.5
  [41ab1584] + InvertedIndices v1.3.1
  [82899510] + IteratorInterfaceExtensions v1.0.0
⌅ [98e50ef6] + JuliaFormatter v1.0.62
  [b964fa9f] + LaTeXStrings v1.4.0
  [0e77f7df] + LazilyInitializedFields v1.3.0
⌅ [23992714] + MAT v0.10.7
  [3da0fdf6] + MPIPreferences v0.1.11
  [1914dd2f] + MacroTools v0.5.16
  [d0879d2d] + MarkdownAST v0.1.2
  [b51810bb] + MatrixDepot v1.0.13
  [4d4711f2] + MatrixMarket v0.5.2
  [15e1cf62] + NPZ v0.4.3
  [2dfb63ee] + PooledArrays v1.4.3
  [08abe8d2] + PrettyTables v3.1.2
  [817f1d60] + ReTestItems v1.35.0
  [2792f1a3] + RegistryInstances v0.1.0
  [6c6a2e73] + Scratch v1.3.0
  [91c51154] + SentinelArrays v1.4.8
  [699a6c99] + SimpleTraits v0.9.5
  [47aef6b3] + SimpleWeightedGraphs v1.5.1
  [860ef19b] + StableRNGs v1.0.4
  [90137ffa] + StaticArrays v1.9.15
  [1e83bf80] + StaticArraysCore v1.4.4
  [892a3eda] + StringManipulation v0.4.2
  [3783bdb8] + TableTraits v1.0.1
  [bd369af6] + Tables v1.12.1
  [8b7d4fe7] + TensorMarket v0.2.2
  [1e6cf692] + TestEnv v1.103.0
  [b718987f] + TextWrap v1.0.2
  [0796e94c] + Tokenize v0.5.29
  [3bb67fe8] + TranscodingStreams v0.11.3
  [a5390f91] + ZipFile v0.10.1
  [2e619515] + Expat_jll v2.7.3+0
  [020c3dae] + Git_LFS_jll v3.7.0+0
  [f8c6e375] + Git_jll v2.52.0+0
⌅ [0234f1f7] + HDF5_jll v1.14.6+0
  [e33a78d0] + Hwloc_jll v2.12.2+0
  [94ce4f54] + Libiconv_jll v1.18.0+0
  [7cb0a576] + MPICH_jll v4.3.2+0
  [f1f71cc9] + MPItrampoline_jll v5.5.4+0
  [9237b28f] + MicrosoftMPI_jll v10.1.4+3
  [fe0851c0] + OpenMPI_jll v5.0.9+0
  [9bd350c2] + OpenSSH_jll v10.2.1+0
⌅ [02c8fc9c] + XML2_jll v2.13.9+0
  [a65dc6b1] + Xorg_libpciaccess_jll v0.18.1+0
  [477f73a3] + libaec_jll v1.1.4+0
  [0dad84c5] + ArgTools v1.1.2
  [2a0f44e3] + Base64 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
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.3.0
  [44cfe95a] ~ Pkg ⇒ v1.14.0
  [9abbd945] + Profile v1.11.0
  [3fa0cd96] + REPL v1.11.0
  [1a1011a3] + SharedArrays v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [f489334b] + StyledStrings v1.13.0
  [a4e569a6] + Tar v1.10.0
  [8dfed614] ~ Test ⇒ v1.11.0
  [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
  [458c3c95] + OpenSSL_jll v3.5.4+0
  [efcefdf7] + PCRE2_jll v10.47.0+0
  [83775a58] + Zlib_jll v1.3.1+2
  [3161d3a3] + Zstd_jll v1.5.7+1
  [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. To see why use `status --outdated -m`
        Test Successfully re-resolved
      Status `/tmp/jl_nMFQ9D/Project.toml`
  [1520ce14] AbstractTrees v0.4.5
  [c7e460c6] ArgParse v1.2.0
  [6e4b80f9] BenchmarkTools v1.6.3
  [5a98b6c4] CIndices v0.3.0
  [34da2185] Compat v4.18.1
⌅ [864edb3b] DataStructures v0.18.22
  [e30172f5] Documenter v1.16.1
  [9177782c] Finch v1.2.12
⌃ [86223c79] Graphs v1.13.1
  [f67ccb44] HDF5 v0.17.2
  [682c06a0] JSON v1.3.0
⌅ [98e50ef6] JuliaFormatter v1.0.62
  [b51810bb] MatrixDepot v1.0.13
  [4d4711f2] MatrixMarket v0.5.2
  [15e1cf62] NPZ v0.4.3
  [aea7be01] PrecompileTools v1.3.3
  [817f1d60] ReTestItems v1.35.0
  [ae029012] Requires v1.3.1
  [5969e224] RewriteTools v0.2.4
  [47aef6b3] SimpleWeightedGraphs v1.5.1
  [860ef19b] StableRNGs v1.0.4
  [10745b16] Statistics v1.11.1
  [2913bbd2] StatsBase v0.34.9
  [b33eeca9] SyntaxInterface v0.2.1
  [8b7d4fe7] TensorMarket v0.2.2
  [7b1f6079] FileWatching v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [44cfe95a] Pkg v1.14.0
  [9a3f8284] Random v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_nMFQ9D/Manifest.toml`
  [a4c015fc] ANSIColoredPrinters v0.0.1
  [1520ce14] AbstractTrees v0.4.5
  [66dad0bd] AliasTables v1.1.3
  [c7e460c6] ArgParse v1.2.0
  [ec485272] ArnoldiMethod v0.4.0
  [15f4f7f2] AutoHashEquals v2.2.0
  [6e4b80f9] BenchmarkTools v1.6.3
  [e1450e63] BufferedStreams v1.2.2
  [5a98b6c4] CIndices v0.3.0
  [00ebfdb7] CSTParser v3.4.3
  [79a69506] ChannelBuffers v0.4.2
  [944b1d66] CodecZlib v0.7.8
⌅ [a80b9123] CommonMark v0.8.16
  [34da2185] Compat v4.18.1
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
  [a93c6f00] DataFrames v1.8.1
⌅ [864edb3b] DataStructures v0.18.22
  [e2d170a0] DataValueInterfaces v1.0.0
  [31c24e10] Distributions v0.25.122
  [ffbed154] DocStringExtensions v0.9.5
  [e30172f5] Documenter v1.16.1
  [5789e2e9] FileIO v1.17.1
  [1a297f60] FillArrays v1.15.0
  [9177782c] Finch v1.2.12
  [d7ba0133] Git v1.5.0
  [c27321d9] Glob v1.4.0
⌃ [86223c79] Graphs v1.13.1
  [f67ccb44] HDF5 v0.17.2
  [34004b35] HypergeometricFunctions v0.3.28
  [b5f81e59] IOCapture v1.0.0
  [d25df0c9] Inflate v0.1.5
  [842dd82b] InlineStrings v1.4.5
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.6
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [692b3bcd] JLLWrappers v1.7.1
  [682c06a0] JSON v1.3.0
⌅ [98e50ef6] JuliaFormatter v1.0.62
  [b964fa9f] LaTeXStrings v1.4.0
  [0e77f7df] LazilyInitializedFields v1.3.0
  [2ab3a3ac] LogExpFunctions v0.3.29
⌅ [23992714] MAT v0.10.7
  [3da0fdf6] MPIPreferences v0.1.11
  [1914dd2f] MacroTools v0.5.16
  [d0879d2d] MarkdownAST v0.1.2
  [b51810bb] MatrixDepot v1.0.13
  [4d4711f2] MatrixMarket v0.5.2
  [e1d29d7a] Missings v1.2.0
  [15e1cf62] NPZ v0.4.3
  [bac558e1] OrderedCollections v1.8.1
  [90014a1f] PDMats v0.11.36
  [69de0a69] Parsers v2.8.3
  [2dfb63ee] PooledArrays v1.4.3
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.0
  [08abe8d2] PrettyTables v3.1.2
  [43287f4e] PtrArrays v1.3.0
  [1fd47b50] QuadGK v2.11.2
  [817f1d60] ReTestItems v1.35.0
  [189a3867] Reexport v1.2.2
  [2792f1a3] RegistryInstances v0.1.0
  [ae029012] Requires v1.3.1
  [5969e224] RewriteTools v0.2.4
  [79098fc4] Rmath v0.9.0
  [6c6a2e73] Scratch v1.3.0
  [91c51154] SentinelArrays v1.4.8
  [699a6c99] SimpleTraits v0.9.5
  [47aef6b3] SimpleWeightedGraphs v1.5.1
  [a2af1166] SortingAlgorithms v1.2.2
  [276daf66] SpecialFunctions v2.6.1
  [860ef19b] StableRNGs v1.0.4
  [90137ffa] StaticArrays v1.9.15
  [1e83bf80] StaticArraysCore v1.4.4
  [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
  [ec057cc2] StructUtils v2.6.0
  [b33eeca9] SyntaxInterface v0.2.1
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.12.1
  [8b7d4fe7] TensorMarket v0.2.2
  [1e6cf692] TestEnv v1.103.0
  [b718987f] TextWrap v1.0.2
  [0796e94c] Tokenize v0.5.29
  [3bb67fe8] TranscodingStreams v0.11.3
  [013be700] UnsafeAtomics v0.3.0
  [a5390f91] ZipFile v0.10.1
  [2e619515] Expat_jll v2.7.3+0
  [020c3dae] Git_LFS_jll v3.7.0+0
  [f8c6e375] Git_jll v2.52.0+0
⌅ [0234f1f7] HDF5_jll v1.14.6+0
  [e33a78d0] Hwloc_jll v2.12.2+0
  [94ce4f54] Libiconv_jll v1.18.0+0
  [7cb0a576] MPICH_jll v4.3.2+0
  [f1f71cc9] MPItrampoline_jll v5.5.4+0
  [9237b28f] MicrosoftMPI_jll v10.1.4+3
  [fe0851c0] OpenMPI_jll v5.0.9+0
  [9bd350c2] OpenSSH_jll v10.2.1+0
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [f50d1b31] Rmath_jll v0.5.1+0
⌅ [02c8fc9c] XML2_jll v2.13.9+0
  [a65dc6b1] Xorg_libpciaccess_jll v0.18.1+0
  [477f73a3] libaec_jll v1.1.4+0
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [8ba89e20] Distributed v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching v1.11.0
  [9fa8497b] Future v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [ac6e5ff7] JuliaSyntaxHighlighting v1.13.0
  [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
  [9abbd945] Profile 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...
[ Info: Running enhanced Finch.jl precompilation... (to disable, run `using Preferences; Preferences.set_preferences!("Finch", "precompile_workload" => false; force=true)`). See https://julialang.github.io/PrecompileTools.jl/stable/#Package-developers:-reducing-the-cost-of-precompilation-during-development for more.
[ Info: Precompiling common tensor formats...
[ Info: Precompiling common tensor formats... (estimated: 0 minutes and 8 seconds)
[ Info: Precompiling common tensor operations...

[ Info: Precompiling common tensor operations... (estimated: 6 minutes and 50 seconds)

[ Info: Precompiling common tensor operations... (estimated: 3 minutes and 58 seconds)

[ Info: Precompiling common tensor operations... (estimated: 3 minutes and 27 seconds)

[ Info: Precompiling common tensor operations... (estimated: 2 minutes and 33 seconds)

[ Info: Precompiling common tensor operations... (estimated: 2 minutes and 0 seconds)

[ Info: Precompiling common tensor operations... (estimated: 1 minutes and 25 seconds)

[ Info: Precompiling common tensor operations... (estimated: 0 minutes and 59 seconds)

[ Info: Precompiling common tensor operations... (estimated: 0 minutes and 37 seconds)

[ Info: Precompiling common tensor operations... (estimated: 0 minutes and 18 seconds)

[ Info: Done!

  1 dependency had output during precompilation:
┌ Finch
│  [Output was shown above]
└  
┌ Warning: The call to compilecache failed to create a usable precompiled cache file for Finch [9177782c-1635-4eb9-9bfb-d9dfa25e6bce]
│   exception = Required dependency Base.PkgId(Base.UUID("864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"), "DataStructures") failed to load from a cache file.
└ @ Base loading.jl:2891
[ Info: Running enhanced Finch.jl precompilation... (to disable, run `using Preferences; Preferences.set_preferences!("Finch", "precompile_workload" => false; force=true)`). See https://julialang.github.io/PrecompileTools.jl/stable/#Package-developers:-reducing-the-cost-of-precompilation-during-development for more.
[ Info: Precompiling common tensor formats...
[ Info: Precompiling common tensor formats... (estimated: 0 minutes and 8 seconds)
[ Info: Precompiling common tensor operations...

[ Info: Precompiling common tensor operations... (estimated: 6 minutes and 57 seconds)

[ Info: Precompiling common tensor operations... (estimated: 4 minutes and 1 seconds)

[ Info: Precompiling common tensor operations... (estimated: 3 minutes and 28 seconds)

[ Info: Precompiling common tensor operations... (estimated: 2 minutes and 34 seconds)

[ Info: Precompiling common tensor operations... (estimated: 2 minutes and 1 seconds)

[ Info: Precompiling common tensor operations... (estimated: 1 minutes and 26 seconds)

[ Info: Precompiling common tensor operations... (estimated: 0 minutes and 59 seconds)

[ Info: Precompiling common tensor operations... (estimated: 0 minutes and 38 seconds)

[ Info: Precompiling common tensor operations... (estimated: 0 minutes and 18 seconds)

[ Info: Done!
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2665
  [4] macro expansion
    @ ./loading.jl:2593 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2557
  [7] require
    @ ./loading.jl:2533 [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/Finch/ucnKV/ext/SparseArraysExt.jl:3
 [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:3250
 [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:3092
 [18] include_string
    @ ./loading.jl:3102 [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/Finch/ucnKV/ext/SparseArraysExt.jl:1
in expression starting at stdin:5

  2 dependencies had output during precompilation:
┌ Finch → SparseArraysExt
│  [Output was shown above]
└  
┌ Finch
│  [ Info: Running enhanced Finch.jl precompilation... (to disable, run `using Preferences; Preferences.set_preferences!("Finch", "precompile_workload" => false; force=true)`). See https://julialang.github.io/PrecompileTools.jl/stable/#Package-developers:-reducing-the-cost-of-precompilation-during-development for more.
│  [ Info: Precompiling common tensor formats...
│  [ Info: Precompiling common tensor formats... (estimated: 0 minutes and 8 seconds)
│  [ Info: Precompiling common tensor operations...
│  
│  [ Info: Precompiling common tensor operations... (estimated: 6 minutes and 57 seconds)
│  
│  [ Info: Precompiling common tensor operations... (estimated: 4 minutes and 1 seconds)
│  
│  [ Info: Precompiling common tensor operations... (estimated: 3 minutes and 28 seconds)
│  
│  [ Info: Precompiling common tensor operations... (estimated: 2 minutes and 34 seconds)
│  
│  [ Info: Precompiling common tensor operations... (estimated: 2 minutes and 1 seconds)
│  
│  [ Info: Precompiling common tensor operations... (estimated: 1 minutes and 26 seconds)
│  
│  [ Info: Precompiling common tensor operations... (estimated: 0 minutes and 59 seconds)
│  
│  [ Info: Precompiling common tensor operations... (estimated: 0 minutes and 38 seconds)
│  
│  [ Info: Precompiling common tensor operations... (estimated: 0 minutes and 18 seconds)
│  
│  [ Info: Done!
└  
┌ Error: Error during loading of extension SparseArraysExt of Finch, use `Base.retry_load_extensions()` to retry.
│   exception =
│    1-element ExceptionStack:
│    The following 1 package failed to precompile:
│    
│    SparseArraysExt 
│    Failed to precompile SparseArraysExt [9408d6b2-d5e1-5f3b-85be-6bbaf7a4cef0] to "/home/pkgeval/.julia/compiled/v1.14/SparseArraysExt/jl_GRq49x" (ProcessExited(1)).
│    
└ @ Base loading.jl:1721
[ Info: Running test item: algebra
WARNING: Method definition assemble!_generator(Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition assemble!_code(Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition assemble!(Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition getindex_helper_generator(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition getindex_helper_code(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition getindex_helper(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition setindex_helper_generator(Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition setindex_helper_code(Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition setindex_helper(Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition helper_equal_generator(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition helper_equal_code(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition helper_equal(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition helper_isequal_generator(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition helper_isequal_code(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition helper_isequal(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition copyto_helper!_generator(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition copyto_helper!_code(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition copyto_helper!(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition dropfills_helper!_generator(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition dropfills_helper!_code(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition dropfills_helper!(Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition reshape_constructor_generator(Any, Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition reshape_constructor_code(Any, Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition reshape_constructor(Any, Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition reshape_kernel_generator(Any, Any, Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition reshape_kernel_code(Any, Any, Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition reshape_kernel(Any, Any, Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition execute_impl_generator(Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:40 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition execute_impl_code(Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:44 overwritten on the same line (check for duplicate calls to `include`).
WARNING: Method definition execute_impl(Any, Any, Any) in module Finch at /home/pkgeval/.julia/packages/Finch/ucnKV/src/util/staging.jl:48 overwritten on the same line (check for duplicate calls to `include`).
[ Info: Running test item: constructors
disagreement with reference output
reference
Tensor(SparseDict(Element(0))) constructors:
initialized tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}([2.0, 2.0, 3.0, 3.0]), 6, [1, 5], [2, 3, 5, 6], [1, 2, 3, 4], Dict((1, 2) => 1, (1, 3) => 2, (1, 6) => 4, (1, 5) => 3), Int64[]))
initialized tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}([2.0, 2.0, 3.0, 3.0]), 6, [1, 5], Int16[2, 3, 5, 6], [1, 2, 3, 4], Dict{Tuple{Int64, Int16}, Int64}((1, 2) => 1, (1, 3) => 2, (1, 6) => 4, (1, 5) => 3), Int64[]))
sized tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}(Float64[]), 7, [1], Int64[], Int64[], Dict{Tuple{Int64, Int64}, Int64}(), Int64[]))
sized tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}(Float64[]), 7, [1], Int16[], Int64[], Dict{Tuple{Int64, Int16}, Int64}(), Int64[]))
empty tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}(Float64[]), 0, [1], Int64[], Int64[], Dict{Tuple{Int64, Int64}, Int64}(), Int64[]))
empty tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}(Float64[]), 0, [1], Int16[], Int64[], Dict{Tuple{Int64, Int16}, Int64}(), Int64[]))


result
Tensor(SparseDict(Element(0))) constructors:
initialized tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}([2.0, 2.0, 3.0, 3.0]), 6, [1, 5], [2, 3, 5, 6], [1, 2, 3, 4], Dict((1, 2) => 1, (1, 3) => 2, (1, 5) => 3, (1, 6) => 4), Int64[]))
initialized tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}([2.0, 2.0, 3.0, 3.0]), 6, [1, 5], Int16[2, 3, 5, 6], [1, 2, 3, 4], Dict{Tuple{Int64, Int16}, Int64}((1, 2) => 1, (1, 3) => 2, (1, 5) => 3, (1, 6) => 4), Int64[]))
sized tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}(Float64[]), 7, [1], Int64[], Int64[], Dict{Tuple{Int64, Int64}, Int64}(), Int64[]))
sized tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}(Float64[]), 7, [1], Int16[], Int64[], Dict{Tuple{Int64, Int16}, Int64}(), Int64[]))
empty tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}(Float64[]), 0, [1], Int64[], Int64[], Dict{Tuple{Int64, Int64}, Int64}(), Int64[]))
empty tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}(Float64[]), 0, [1], Int16[], Int64[], Dict{Tuple{Int64, Int16}, Int64}(), Int64[]))



Construct SparseDict: Test Failed at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/constructors_tests.jl:116
  Expression: check_output("constructors/format_$(key).txt", String(take!(io)))
   Evaluated: check_output("constructors/format_SparseDict.txt", "Tensor(SparseDict(Element(0))) constructors:\ninitialized tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}([2.0, 2.0, 3.0, 3.0]), 6, [1, 5], [2, 3, 5, 6], [1, 2, 3, 4], Dict((1, 2) => 1, (1, 3) => 2, (1, 5) => 3, (1, 6) => 4), Int64[]))\ninitialized tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}([2.0, 2.0, 3.0, 3.0]), 6, [1, 5], Int16[2, 3, 5, 6], [1, 2, 3, 4], Dict{Tuple{Int64, Int16}, Int64}((1, 2) => 1, (1, 3) => 2, (1, 5) => 3, (1, 6) => 4), Int64[]))\nsized tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}(Float64[]), 7, [1], Int64[], Int64[], Dict{Tuple{Int64, Int64}, Int64}(), Int64[]))\nsized tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}(Float64[]), 7, [1], Int16[], Int64[], Dict{Tuple{Int64, Int16}, Int64}(), Int64[]))\nempty tensor: Tensor(SparseDict{Int64}(Element{0.0, Float64, Int64}(Float64[]), 0, [1], Int64[], Int64[], Dict{Tuple{Int64, Int64}, Int64}(), Int64[]))\nempty tensor: Tensor(SparseDict{Int16}(Element{0.0, Float64, Int64}(Float64[]), 0, [1], Int16[], Int64[], Dict{Tuple{Int64, Int16}, Int64}(), Int64[]))\n")
  Stacktrace:
   [1] top-level scope
     @ ~/.julia/packages/Finch/ucnKV/test/suites/constructors_tests.jl:27
   [2] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [3] macro expansion
     @ ~/.julia/packages/Finch/ucnKV/test/suites/constructors_tests.jl:116 [inlined]
   [4] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:753 [inlined]
[ Info: Running test item: continuous_examples
[ Info: Running test item: docs
┌ Warning: Unable to determine HTML(edit_link = ...) from remote HEAD branch, defaulting to "master".
│ Calling `git remote` failed with an exception. Set JULIA_DEBUG=Documenter to see the error.
│ Unless this is due to a configuration error, the relevant variable should be set explicitly.
└ @ Documenter ~/.julia/packages/Documenter/xvqbW/src/utilities/utilities.jl:680
[ Info: SetupBuildDirectory: setting up build directory.
[ Info: Doctest: running doctests.
┌ Error: doctest failure in ../src/tensors/tensors.jl:59-65
│ 
│ ```jldoctest
│ julia> println(summary(Tensor(sparse([1 0; 0 1]))))
│ 2×2 Tensor(Dense(SparseList(Element(0))))
│ 
│ julia> println(summary(Tensor(ones(3, 2, 4))))
│ 3×2×4 Tensor(Dense(Dense(Dense(Element(0.0)))))
│ ```
│ 
│ Subexpression:
│ 
│ println(summary(Tensor(sparse([1 0; 0 1]))))
│ 
│ Evaluated output:
│ 
│ 2×2 Tensor(Dense(Dense(Element(0))))
│ 
│ Expected output:
│ 
│ 2×2 Tensor(Dense(SparseList(Element(0))))
│ 
│   diff =
│    Warning: Diff output requires color.
│    2×2 Tensor(Dense(SparseList(Element(0))))Tensor(Dense(Dense(Element(0))))
└ @ Documenter ~/.julia/packages/Finch/ucnKV/src/tensors/tensors.jl:59
┌ Error: Doctesting failed
│   exception =
│    `makedocs` encountered a doctest error. Terminating build
│    Stacktrace:
│      [1] error(s::String)
│        @ Base ./error.jl:44
│      [2] runner(::Type{Documenter.Builder.Doctest}, doc::Documenter.Document)
│        @ Documenter ~/.julia/packages/Documenter/xvqbW/src/builder_pipeline.jl:213
│      [3] dispatch(::Type{Documenter.Builder.DocumentPipeline}, x::Documenter.Document)
│        @ Documenter.Selectors ~/.julia/packages/Documenter/xvqbW/src/utilities/Selectors.jl:170
│      [4] #89
│        @ ~/.julia/packages/Documenter/xvqbW/src/makedocs.jl:283 [inlined]
│      [5] withenv(::Documenter.var"#89#90"{Documenter.Document}, ::Pair{String, Nothing}, ::Vararg{Pair{String, Nothing}})
│        @ Base ./env.jl:265
│      [6] #87
│        @ ~/.julia/packages/Documenter/xvqbW/src/makedocs.jl:282 [inlined]
│      [7] cd(f::Documenter.var"#87#88"{Documenter.Document}, dir::String)
│        @ Base.Filesystem ./file.jl:112
│      [8] makedocs(; debug::Bool, format::Documenter.HTMLWriter.HTML, kwargs::@Kwargs{root::String, source::String, sitename::String, doctest::Symbol, modules::Vector{Module}, doctestfilters::Vector{Regex}, remotes::Nothing, plugins::Vector{Documenter.Plugin}})
│        @ Documenter ~/.julia/packages/Documenter/xvqbW/src/makedocs.jl:281
│      [9] kwcall(::@NamedTuple{root::String, source::String, sitename::String, doctest::Symbol, modules::Vector{Module}, doctestfilters::Vector{Regex}, remotes::Nothing, plugins::Vector{Documenter.Plugin}}, ::typeof(makedocs))
│        @ Documenter ~/.julia/packages/Documenter/xvqbW/src/makedocs.jl:274
│     [10] (::Documenter.var"#all_doctests#141"{Bool, Vector{Regex}, Vector{Documenter.Plugin}, Vector{Module}})()
│        @ Documenter ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:85
│     [11] macro expansion
│        @ ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:110 [inlined]
│     [12] macro expansion
│        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:750 [inlined]
│     [13] macro expansion
│        @ ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:110 [inlined]
│     [14] macro expansion
│        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
│     [15] doctest(source::String, modules::Vector{Module}; fix::Bool, testset::String, doctestfilters::Vector{Regex}, plugins::Vector{Documenter.Plugin})
│        @ Documenter ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:110
│     [16] doctest
│        @ ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:69 [inlined]
│     [17] doctest(package::Module; manual::Bool, testset::Nothing, kwargs::@Kwargs{})
│        @ Documenter ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:37
│     [18] doctest(package::Module)
│        @ Documenter ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:20
│     [19] top-level scope
│        @ ~/.julia/packages/Finch/ucnKV/docs/test.jl:16
│     [20] include(mapexpr::Function, mod::Module, _path::String)
│        @ Base ./Base.jl:310
│     [21] top-level scope
│        @ ~/.julia/packages/Finch/ucnKV/test/suites/docs_tests.jl:10
│     [22] eval(m::Module, e::Any)
│        @ Core ./boot.jl:489
│     [23] top-level scope
│        @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:136
│     [24] macro expansion
│        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
│     [25] macro expansion
│        @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:137 [inlined]
│     [26] include(mapexpr::Function, mod::Module, _path::String)
│        @ Base ./Base.jl:310
│     [27] top-level scope
│        @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:159
│     [28] macro expansion
│        @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
│     [29] macro expansion
│        @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:164 [inlined]
│     [30] include(mapexpr::Function, mod::Module, _path::String)
│        @ Base ./Base.jl:310
│     [31] top-level scope
│        @ none:6
│     [32] eval(m::Module, e::Any)
│        @ Core ./boot.jl:489
│     [33] exec_options(opts::Base.JLOptions)
│        @ Base ./client.jl:310
│     [34] _start()
│        @ Base ./client.jl:577
└ @ Documenter ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:99

Doctests: Finch: Test Failed at /home/pkgeval/.julia/packages/Documenter/xvqbW/src/doctest.jl:110
  Expression: all_doctests()
  Stacktrace:
   [1] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:753 [inlined]
   [2] macro expansion
     @ ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:110 [inlined]
   [3] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [4] doctest(source::String, modules::Vector{Module}; fix::Bool, testset::String, doctestfilters::Vector{Regex}, plugins::Vector{Documenter.Plugin})
     @ Documenter ~/.julia/packages/Documenter/xvqbW/src/doctest.jl:110
[ Info: Running test item: examples

pagerank: Test Failed at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/examples_tests.jl:30
  Expression: maximum(abs.(output .- expected)) < tol
   Evaluated: 0.012212010923306649 < 1.0e-6
  Stacktrace:
   [1] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:753 [inlined]
   [2] macro expansion
     @ ~/.julia/packages/Finch/ucnKV/test/suites/examples_tests.jl:30 [inlined]
   [3] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [4] top-level scope
     @ ~/.julia/packages/Finch/ucnKV/test/suites/examples_tests.jl:17
[ Info: Running test item: fileio
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2665
  [4] macro expansion
    @ ./loading.jl:2593 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2557
  [7] require
    @ ./loading.jl:2533 [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/Finch/ucnKV/ext/SparseArraysExt.jl:3
 [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:3250
 [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:3092
 [18] include_string
    @ ./loading.jl:3102 [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/Finch/ucnKV/ext/SparseArraysExt.jl:1
in expression starting at stdin:5
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2665
  [4] macro expansion
    @ ./loading.jl:2593 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2557
  [7] require
    @ ./loading.jl:2533 [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/Finch/ucnKV/ext/HDF5Ext.jl:3
 [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:3250
 [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:3092
 [18] include_string
    @ ./loading.jl:3102 [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/Finch/ucnKV/ext/HDF5Ext.jl:1
in expression starting at stdin:5

  2 dependencies had output during precompilation:
┌ Finch → SparseArraysExt
│  ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
│    [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
│      @ Base ./loading.jl:2665
│    [4] macro expansion
│      @ ./loading.jl:2593 [inlined]
│    [5] macro expansion
│      @ ./lock.jl:376 [inlined]
│    [6] __require(into::Module, mod::Symbol)
│      @ Base ./loading.jl:2557
│    [7] require
│      @ ./loading.jl:2533 [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/Finch/ucnKV/ext/SparseArraysExt.jl:3
│   [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:3250
│   [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:3092
│   [18] include_string
│      @ ./loading.jl:3102 [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/Finch/ucnKV/ext/SparseArraysExt.jl:1
│  in expression starting at stdin:5
└  
┌ Finch → HDF5Ext
│  [Output was shown above]
└  
┌ Error: Error during loading of extension HDF5Ext of Finch, use `Base.retry_load_extensions()` to retry.
│   exception =
│    1-element ExceptionStack:
│    The following 2 packages failed to precompile:
│    
│    SparseArraysExt 
│    Failed to precompile SparseArraysExt [9408d6b2-d5e1-5f3b-85be-6bbaf7a4cef0] to "/home/pkgeval/.julia/compiled/v1.14/SparseArraysExt/jl_oGo7KX" (ProcessExited(1)).
│    
│    HDF5Ext 
│    Failed to precompile HDF5Ext [a758ecf6-f631-530b-85bc-2cfb0f269268] to "/home/pkgeval/.julia/compiled/v1.14/HDF5Ext/jl_ZP8CvF" (ProcessExited(1)).
│    
└ @ Base loading.jl:1721

h5 binsparse: Error During Test at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/fileio_tests.jl:6
  Got exception outside of a @test
  Finch.FinchExtensionError("HDF5.jl must be loaded to write .bsp.h5 files (hint: `using HDF5`)")
  Stacktrace:
    [1] bspwrite_h5(::String, ::Vararg{Any})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/binsparse.jl:247
    [2] bspwrite(fname::String, arr::Tensor{SparseCOOLevel{2, Tuple{Int64, Int64}, Vector{Int64}, Tuple{Vector{Int64}, Vector{Int64}}, ElementLevel{0.0, Float64, Int64, Vector{Float64}}}}, attrs::OrderedCollections.OrderedDict{Any, Any})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/binsparse.jl:263
    [3] bspwrite(fname::String, arr::Tensor{SparseCOOLevel{2, Tuple{Int64, Int64}, Vector{Int64}, Tuple{Vector{Int64}, Vector{Int64}}, ElementLevel{0.0, Float64, Int64, Vector{Float64}}}})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/binsparse.jl:262
    [4] fwrite(filename::String, tns::Tensor{SparseCOOLevel{2, Tuple{Int64, Int64}, Vector{Int64}, Tuple{Vector{Int64}, Vector{Int64}}, ElementLevel{0.0, Float64, Int64, Vector{Float64}}}})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/fileio.jl:20
    [5] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/suites/fileio_tests.jl:7
    [6] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
    [7] macro expansion
      @ ~/.julia/packages/Finch/ucnKV/test/suites/fileio_tests.jl:14 [inlined]
    [8] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
    [9] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:136
   [10] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [11] macro expansion
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:137 [inlined]
   [12] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [13] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:159
   [14] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [15] macro expansion
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:166 [inlined]
   [16] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [17] top-level scope
      @ none:6
   [18] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [19] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [20] _start()
      @ Base ./client.jl:577
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2665
  [4] macro expansion
    @ ./loading.jl:2593 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2557
  [7] require
    @ ./loading.jl:2533 [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/Finch/ucnKV/ext/SparseArraysExt.jl:3
 [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:3250
 [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:3092
 [18] include_string
    @ ./loading.jl:3102 [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/Finch/ucnKV/ext/SparseArraysExt.jl:1
in expression starting at stdin:5
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2665
  [4] macro expansion
    @ ./loading.jl:2593 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2557
  [7] require
    @ ./loading.jl:2533 [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/Finch/ucnKV/ext/NPZExt.jl:3
 [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:3250
 [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:3092
 [18] include_string
    @ ./loading.jl:3102 [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/Finch/ucnKV/ext/NPZExt.jl:1
in expression starting at stdin:5

  2 dependencies had output during precompilation:
┌ Finch → SparseArraysExt
│  ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
│    [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
│      @ Base ./loading.jl:2665
│    [4] macro expansion
│      @ ./loading.jl:2593 [inlined]
│    [5] macro expansion
│      @ ./lock.jl:376 [inlined]
│    [6] __require(into::Module, mod::Symbol)
│      @ Base ./loading.jl:2557
│    [7] require
│      @ ./loading.jl:2533 [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/Finch/ucnKV/ext/SparseArraysExt.jl:3
│   [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:3250
│   [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:3092
│   [18] include_string
│      @ ./loading.jl:3102 [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/Finch/ucnKV/ext/SparseArraysExt.jl:1
│  in expression starting at stdin:5
└  
┌ Finch → NPZExt
│  [Output was shown above]
└  
┌ Error: Error during loading of extension NPZExt of Finch, use `Base.retry_load_extensions()` to retry.
│   exception =
│    1-element ExceptionStack:
│    The following 2 packages failed to precompile:
│    
│    SparseArraysExt 
│    Failed to precompile SparseArraysExt [9408d6b2-d5e1-5f3b-85be-6bbaf7a4cef0] to "/home/pkgeval/.julia/compiled/v1.14/SparseArraysExt/jl_QCwsCN" (ProcessExited(1)).
│    
│    NPZExt 
│    Failed to precompile NPZExt [b1063bb2-796d-599f-9fbf-55925323154b] to "/home/pkgeval/.julia/compiled/v1.14/NPZExt/jl_YMQpE4" (ProcessExited(1)).
│    
└ @ Base loading.jl:1721

npy binsparse: Error During Test at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/fileio_tests.jl:71
  Got exception outside of a @test
  Finch.FinchExtensionError("NPZ.jl must be loaded to write .bspnpy files (hint: `using NPZ`)")
  Stacktrace:
    [1] bspwrite_bspnpy(::String, ::Vararg{Any})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/binsparse.jl:254
    [2] bspwrite(fname::String, arr::Tensor{SparseCOOLevel{2, Tuple{Int64, Int64}, Vector{Int64}, Tuple{Vector{Int64}, Vector{Int64}}, ElementLevel{0.0, Float64, Int64, Vector{Float64}}}}, attrs::OrderedCollections.OrderedDict{Any, Any})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/binsparse.jl:265
    [3] bspwrite(fname::String, arr::Tensor{SparseCOOLevel{2, Tuple{Int64, Int64}, Vector{Int64}, Tuple{Vector{Int64}, Vector{Int64}}, ElementLevel{0.0, Float64, Int64, Vector{Float64}}}})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/binsparse.jl:262
    [4] fwrite(filename::String, tns::Tensor{SparseCOOLevel{2, Tuple{Int64, Int64}, Vector{Int64}, Tuple{Vector{Int64}, Vector{Int64}}, ElementLevel{0.0, Float64, Int64, Vector{Float64}}}})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/fileio.jl:20
    [5] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/suites/fileio_tests.jl:72
    [6] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
    [7] macro expansion
      @ ~/.julia/packages/Finch/ucnKV/test/suites/fileio_tests.jl:79 [inlined]
    [8] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
    [9] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:136
   [10] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [11] macro expansion
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:137 [inlined]
   [12] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [13] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:159
   [14] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [15] macro expansion
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:166 [inlined]
   [16] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [17] top-level scope
      @ none:6
   [18] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [19] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [20] _start()
      @ Base ./client.jl:577
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2665
  [4] macro expansion
    @ ./loading.jl:2593 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2557
  [7] require
    @ ./loading.jl:2533 [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/Finch/ucnKV/ext/SparseArraysExt.jl:3
 [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:3250
 [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:3092
 [18] include_string
    @ ./loading.jl:3102 [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/Finch/ucnKV/ext/SparseArraysExt.jl:1
in expression starting at stdin:5
ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
  [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
    @ Base ./loading.jl:2665
  [4] macro expansion
    @ ./loading.jl:2593 [inlined]
  [5] macro expansion
    @ ./lock.jl:376 [inlined]
  [6] __require(into::Module, mod::Symbol)
    @ Base ./loading.jl:2557
  [7] require
    @ ./loading.jl:2533 [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/Finch/ucnKV/ext/TensorMarketExt.jl:3
 [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:3250
 [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:3092
 [18] include_string
    @ ./loading.jl:3102 [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/Finch/ucnKV/ext/TensorMarketExt.jl:1
in expression starting at stdin:5

  2 dependencies had output during precompilation:
┌ Finch → SparseArraysExt
│  ERROR: LoadError: Precompiled image Base.PkgId(Base.UUID("9177782c-1635-4eb9-9bfb-d9dfa25e6bce"), "Finch") 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:2813
│    [3] _require_prelocked(uuidkey::Base.PkgId, env::String)
│      @ Base ./loading.jl:2665
│    [4] macro expansion
│      @ ./loading.jl:2593 [inlined]
│    [5] macro expansion
│      @ ./lock.jl:376 [inlined]
│    [6] __require(into::Module, mod::Symbol)
│      @ Base ./loading.jl:2557
│    [7] require
│      @ ./loading.jl:2533 [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/Finch/ucnKV/ext/SparseArraysExt.jl:3
│   [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:3250
│   [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:3092
│   [18] include_string
│      @ ./loading.jl:3102 [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/Finch/ucnKV/ext/SparseArraysExt.jl:1
│  in expression starting at stdin:5
└  
┌ Finch → TensorMarketExt
│  [Output was shown above]
└  
┌ Error: Error during loading of extension TensorMarketExt of Finch, use `Base.retry_load_extensions()` to retry.
│   exception =
│    1-element ExceptionStack:
│    The following 2 packages failed to precompile:
│    
│    SparseArraysExt 
│    Failed to precompile SparseArraysExt [9408d6b2-d5e1-5f3b-85be-6bbaf7a4cef0] to "/home/pkgeval/.julia/compiled/v1.14/SparseArraysExt/jl_APKELi" (ProcessExited(1)).
│    
│    TensorMarketExt 
│    Failed to precompile TensorMarketExt [54040247-0909-5821-a256-61e2e9969419] to "/home/pkgeval/.julia/compiled/v1.14/TensorMarketExt/jl_IxmEvo" (ProcessExited(1)).
│    
└ @ Base loading.jl:1721

fileio: Error During Test at /home/pkgeval/.julia/packages/Finch/ucnKV/test/runtests.jl:135
  Got exception outside of a @test
  Finch.FinchExtensionError("TensorMarket.jl must be loaded to use write .ttx files (hint: `using TensorMarket`)")
  Stacktrace:
    [1] fttwrite(::String, ::Vararg{Any})
      @ Finch ~/.julia/packages/Finch/ucnKV/src/interface/fileio/tensormarket.jl:10
    [2] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/suites/fileio_tests.jl:139
    [3] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
    [4] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:136
    [5] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
    [6] macro expansion
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:137 [inlined]
    [7] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
    [8] top-level scope
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:159
    [9] macro expansion
      @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [10] macro expansion
      @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:166 [inlined]
   [11] include(mapexpr::Function, mod::Module, _path::String)
      @ Base ./Base.jl:310
   [12] top-level scope
      @ none:6
   [13] eval(m::Module, e::Any)
      @ Core ./boot.jl:489
   [14] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:310
   [15] _start()
      @ Base ./client.jl:577
[ Info: Running test item: galley
┌ Warning: Conversion to OrderedDict is deprecated for unordered associative containers (in this case, Dict{Symbol, Int64}). Use an ordered or sorted associative type, such as SortedDict and OrderedDict.
│   caller = TensorDef(index_set::StableSet{Symbol}, dim_sizes::Dict{Symbol, Int64}, fill_val::Float64, level_formats::Nothing, index_order::Nothing, index_protocols::Nothing) at tensor-stats.jl:6
└ @ Core ~/.julia/packages/Finch/ucnKV/src/Galley/TensorStats/tensor-stats.jl:6
┌ Warning: Conversion to OrderedDict is deprecated for unordered associative containers (in this case, Dict{Symbol, Int64}). Use an ordered or sorted associative type, such as SortedDict and OrderedDict.
│   caller = DCStats(def::TensorDef, idx_2_int::Dict{Symbol, Int64}, int_2_idx::Dict{Int64, Symbol}, dcs::StableSet{Finch.Galley.DegreeConstraint}) at tensor-stats.jl:267
└ @ Core ~/.julia/packages/Finch/ucnKV/src/Galley/TensorStats/tensor-stats.jl:267
┌ Warning: Conversion to OrderedDict is deprecated for unordered associative containers (in this case, Dict{Int64, Symbol}). Use an ordered or sorted associative type, such as SortedDict and OrderedDict.
│   caller = DCStats(def::TensorDef, idx_2_int::Dict{Symbol, Int64}, int_2_idx::Dict{Int64, Symbol}, dcs::StableSet{Finch.Galley.DegreeConstraint}) at tensor-stats.jl:267
└ @ Core ~/.julia/packages/Finch/ucnKV/src/Galley/TensorStats/tensor-stats.jl:267
┌ Warning: Conversion to OrderedDict is deprecated for unordered associative containers (in this case, Dict{Symbol, Int64}). Use an ordered or sorted associative type, such as SortedDict and OrderedDict.
│   caller = TensorDef(index_set::StableSet{Symbol}, dim_sizes::Dict{Symbol, Int64}, fill_val::Int64, level_formats::Nothing, index_order::Nothing, index_protocols::Nothing) at tensor-stats.jl:6
└ @ Core ~/.julia/packages/Finch/ucnKV/src/Galley/TensorStats/tensor-stats.jl:6
[ Info: Running test item: index
disagreement with reference output
reference
begin
    C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
    C_lvl_ptr = C_lvl.ptr
    C_lvl_idx = C_lvl.idx
    C_lvl_stop = C_lvl.shape
    C_lvl_2 = C_lvl.lvl
    C_lvl_2_val = C_lvl_2.val
    A_lvl = (((ex.bodies[1]).bodies[2]).body.rhs.args[1]).tns.bind.lvl
    A_lvl_ptr = A_lvl.ptr
    A_lvl_idx = A_lvl.idx
    A_lvl_stop = A_lvl.shape
    A_lvl_2 = A_lvl.lvl
    A_lvl_2_val = A_lvl_2.val
    B_lvl = (((ex.bodies[1]).bodies[2]).body.rhs.args[2]).tns.bind.lvl
    B_lvl_ptr = B_lvl.ptr
    B_lvl_idx = B_lvl.idx
    B_lvl_stop = B_lvl.shape
    B_lvl_2 = B_lvl.lvl
    B_lvl_2_val = B_lvl_2.val
    C_lvl_qos_stop = 0
    Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
    Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
    C_lvl_qos = 0 + 1
    0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
    phase_stop = min(C_lvl_stop, 0)
    if phase_stop >= 1
        for i_6 = 1:phase_stop
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvl_2_val[C_lvl_qos] = missing
            C_lvl_idx[C_lvl_qos] = i_6
            C_lvl_qos += 1
        end
    end
    phase_stop_2 = min(C_lvl_stop, 10, A_lvl_stop)
    if phase_stop_2 >= 1
        A_lvl_q = A_lvl_ptr[1]
        A_lvl_q_stop = A_lvl_ptr[1 + 1]
        if A_lvl_q < A_lvl_q_stop
            A_lvl_i1 = A_lvl_idx[A_lvl_q_stop - 1]
        else
            A_lvl_i1 = 0
        end
        phase_stop_3 = min(A_lvl_i1, phase_stop_2)
        if phase_stop_3 >= 1
            if A_lvl_idx[A_lvl_q] < 1
                A_lvl_q = Finch.scansearch(A_lvl_idx, 1, A_lvl_q, A_lvl_q_stop - 1)
            end
            while true
                A_lvl_i = A_lvl_idx[A_lvl_q]
                if A_lvl_i < phase_stop_3
                    A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_2
                    C_lvl_idx[C_lvl_qos] = A_lvl_i
                    C_lvl_qos += 1
                    A_lvl_q += 1
                else
                    phase_stop_5 = min(phase_stop_3, A_lvl_i)
                    if A_lvl_i == phase_stop_5
                        A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
                        if C_lvl_qos > C_lvl_qos_stop
                            C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                            Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                            Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                            Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                        end
                        C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_2
                        C_lvl_idx[C_lvl_qos] = phase_stop_5
                        C_lvl_qos += 1
                        A_lvl_q += 1
                    end
                    break
                end
            end
        end
    end
    phase_start_6 = max(1, 1 + A_lvl_stop)
    phase_stop_7 = min(C_lvl_stop, 10)
    if phase_stop_7 >= phase_start_6
        for i_14 = phase_start_6:phase_stop_7
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvl_2_val[C_lvl_qos] = missing
            C_lvl_idx[C_lvl_qos] = i_14
            C_lvl_qos += 1
        end
    end
    phase_stop_8 = min(C_lvl_stop, 0, 10 + B_lvl_stop)
    if phase_stop_8 >= 11
        B_lvl_q = B_lvl_ptr[1]
        B_lvl_q_stop = B_lvl_ptr[1 + 1]
        if B_lvl_q < B_lvl_q_stop
            B_lvl_i1 = B_lvl_idx[B_lvl_q_stop - 1]
        else
            B_lvl_i1 = 0
        end
        phase_stop_9 = min(phase_stop_8, 10 + B_lvl_i1)
        if phase_stop_9 >= 11
            if B_lvl_idx[B_lvl_q] < -10 + 11
                B_lvl_q = Finch.scansearch(B_lvl_idx, -10 + 11, B_lvl_q, B_lvl_q_stop - 1)
            end
            while true
                B_lvl_i = B_lvl_idx[B_lvl_q]
                phase_stop_10 = 10 + B_lvl_i
                if phase_stop_10 < phase_stop_9
                    B_lvl_2_val_2 = B_lvl_2_val[B_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvl_2_val[C_lvl_qos] = B_lvl_2_val_2
                    C_lvl_idx[C_lvl_qos] = phase_stop_10
                    C_lvl_qos += 1
                    B_lvl_q += 1
                else
                    phase_stop_11 = min(phase_stop_9, 10 + B_lvl_i)
                    if B_lvl_i == -10 + phase_stop_11
                        B_lvl_2_val_2 = B_lvl_2_val[B_lvl_q]
                        if C_lvl_qos > C_lvl_qos_stop
                            C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                            Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                            Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                            Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                        end
                        C_lvl_2_val[C_lvl_qos] = B_lvl_2_val_2
                        C_lvl_idx[C_lvl_qos] = phase_stop_11
                        C_lvl_qos += 1
                        B_lvl_q += 1
                    end
                    break
                end
            end
        end
    end
    phase_stop_13 = min(C_lvl_stop, A_lvl_stop, 10 + B_lvl_stop)
    if phase_stop_13 >= 11
        A_lvl_q = A_lvl_ptr[1]
        A_lvl_q_stop = A_lvl_ptr[1 + 1]
        if A_lvl_q < A_lvl_q_stop
            A_lvl_i1 = A_lvl_idx[A_lvl_q_stop - 1]
        else
            A_lvl_i1 = 0
        end
        B_lvl_q = B_lvl_ptr[1]
        B_lvl_q_stop = B_lvl_ptr[1 + 1]
        if B_lvl_q < B_lvl_q_stop
            B_lvl_i1 = B_lvl_idx[B_lvl_q_stop - 1]
        else
            B_lvl_i1 = 0
        end
        phase_stop_14 = min(A_lvl_i1, 10 + B_lvl_i1, phase_stop_13)
        if phase_stop_14 >= 11
            i = 11
            if A_lvl_idx[A_lvl_q] < 11
                A_lvl_q = Finch.scansearch(A_lvl_idx, 11, A_lvl_q, A_lvl_q_stop - 1)
            end
            if B_lvl_idx[B_lvl_q] < -10 + 11
                B_lvl_q = Finch.scansearch(B_lvl_idx, -10 + 11, B_lvl_q, B_lvl_q_stop - 1)
            end
            while i <= phase_stop_14
                A_lvl_i = A_lvl_idx[A_lvl_q]
                B_lvl_i = B_lvl_idx[B_lvl_q]
                phase_stop_15 = min(A_lvl_i, 10 + B_lvl_i, phase_stop_14)
                if A_lvl_i == phase_stop_15 && B_lvl_i == -10 + phase_stop_15
                    A_lvl_2_val_3 = A_lvl_2_val[A_lvl_q]
                    B_lvl_2_val_3 = B_lvl_2_val[B_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvl_2_val[C_lvl_qos] = coalesce(A_lvl_2_val_3, B_lvl_2_val_3)
                    C_lvl_idx[C_lvl_qos] = phase_stop_15
                    C_lvl_qos += 1
                    A_lvl_q += 1
                    B_lvl_q += 1
                elseif B_lvl_i == -10 + phase_stop_15
                    B_lvl_q += 1
                elseif A_lvl_i == phase_stop_15
                    A_lvl_2_val_3 = A_lvl_2_val[A_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvl_2_val[C_lvl_qos] = coalesce(A_lvl_2_val_3, 0.0)
                    C_lvl_idx[C_lvl_qos] = phase_stop_15
                    C_lvl_qos += 1
                    A_lvl_q += 1
                end
                i = phase_stop_15 + 1
            end
        end
        phase_start_16 = max(11 + B_lvl_i1, 11)
        phase_stop_17 = min(A_lvl_i1, phase_stop_13)
        if phase_stop_17 >= phase_start_16
            if A_lvl_idx[A_lvl_q] < phase_start_16
                A_lvl_q = Finch.scansearch(A_lvl_idx, phase_start_16, A_lvl_q, A_lvl_q_stop - 1)
            end
            while true
                A_lvl_i = A_lvl_idx[A_lvl_q]
                if A_lvl_i < phase_stop_17
                    A_lvl_2_val_4 = A_lvl_2_val[A_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvl_2_val[C_lvl_qos] = coalesce(A_lvl_2_val_4, 0.0)
                    C_lvl_idx[C_lvl_qos] = A_lvl_i
                    C_lvl_qos += 1
                    A_lvl_q += 1
                else
                    phase_stop_19 = min(A_lvl_i, phase_stop_17)
                    if A_lvl_i == phase_stop_19
                        A_lvl_2_val_4 = A_lvl_2_val[A_lvl_q]
                        if C_lvl_qos > C_lvl_qos_stop
                            C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                            Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                            Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                            Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                        end
                        C_lvl_2_val[C_lvl_qos] = coalesce(A_lvl_2_val_4, 0.0)
                        C_lvl_idx[C_lvl_qos] = phase_stop_19
                        C_lvl_qos += 1
                        A_lvl_q += 1
                    end
                    break
                end
            end
        end
    end
    phase_start_19 = max(11, 1 + A_lvl_stop)
    phase_stop_21 = min(C_lvl_stop, 10 + B_lvl_stop)
    if phase_stop_21 >= phase_start_19
        B_lvl_q = B_lvl_ptr[1]
        B_lvl_q_stop = B_lvl_ptr[1 + 1]
        if B_lvl_q < B_lvl_q_stop
            B_lvl_i1 = B_lvl_idx[B_lvl_q_stop - 1]
        else
            B_lvl_i1 = 0
        end
        phase_stop_22 = min(10 + B_lvl_i1, phase_stop_21)
        if phase_stop_22 >= phase_start_19
            if B_lvl_idx[B_lvl_q] < -10 + phase_start_19
                B_lvl_q = Finch.scansearch(B_lvl_idx, -10 + phase_start_19, B_lvl_q, B_lvl_q_stop - 1)
            end
            while true
                B_lvl_i = B_lvl_idx[B_lvl_q]
                phase_stop_23 = 10 + B_lvl_i
                if phase_stop_23 < phase_stop_22
                    B_lvl_2_val_5 = B_lvl_2_val[B_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvl_2_val[C_lvl_qos] = B_lvl_2_val_5
                    C_lvl_idx[C_lvl_qos] = phase_stop_23
                    C_lvl_qos += 1
                    B_lvl_q += 1
                else
                    phase_stop_24 = min(10 + B_lvl_i, phase_stop_22)
                    if B_lvl_i == -10 + phase_stop_24
                        B_lvl_2_val_5 = B_lvl_2_val[B_lvl_q]
                        if C_lvl_qos > C_lvl_qos_stop
                            C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                            Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                            Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                            Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                        end
                        C_lvl_2_val[C_lvl_qos] = B_lvl_2_val_5
                        C_lvl_idx[C_lvl_qos] = phase_stop_24
                        C_lvl_qos += 1
                        B_lvl_q += 1
                    end
                    break
                end
            end
        end
    end
    phase_start_24 = max(1, 11 + B_lvl_stop)
    phase_stop_26 = min(C_lvl_stop, 0)
    if phase_stop_26 >= phase_start_24
        for i_39 = phase_start_24:phase_stop_26
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvl_2_val[C_lvl_qos] = missing
            C_lvl_idx[C_lvl_qos] = i_39
            C_lvl_qos += 1
        end
    end
    phase_start_25 = max(1, 11 + B_lvl_stop)
    phase_stop_27 = min(C_lvl_stop, A_lvl_stop)
    if phase_stop_27 >= phase_start_25
        A_lvl_q = A_lvl_ptr[1]
        A_lvl_q_stop = A_lvl_ptr[1 + 1]
        if A_lvl_q < A_lvl_q_stop
            A_lvl_i1 = A_lvl_idx[A_lvl_q_stop - 1]
        else
            A_lvl_i1 = 0
        end
        phase_stop_28 = min(A_lvl_i1, phase_stop_27)
        if phase_stop_28 >= phase_start_25
            if A_lvl_idx[A_lvl_q] < phase_start_25
                A_lvl_q = Finch.scansearch(A_lvl_idx, phase_start_25, A_lvl_q, A_lvl_q_stop - 1)
            end
            while true
                A_lvl_i = A_lvl_idx[A_lvl_q]
                if A_lvl_i < phase_stop_28
                    A_lvl_2_val_5 = A_lvl_2_val[A_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_5
                    C_lvl_idx[C_lvl_qos] = A_lvl_i
                    C_lvl_qos += 1
                    A_lvl_q += 1
                else
                    phase_stop_30 = min(A_lvl_i, phase_stop_28)
                    if A_lvl_i == phase_stop_30
                        A_lvl_2_val_5 = A_lvl_2_val[A_lvl_q]
                        if C_lvl_qos > C_lvl_qos_stop
                            C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                            Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                            Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                            Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                        end
                        C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_5
                        C_lvl_idx[C_lvl_qos] = phase_stop_30
                        C_lvl_qos += 1
                        A_lvl_q += 1
                    end
                    break
                end
            end
        end
    end
    phase_start_29 = max(1, 1 + A_lvl_stop, 11 + B_lvl_stop)
    if C_lvl_stop >= phase_start_29
        for i_47 = phase_start_29:C_lvl_stop
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvl_2_val[C_lvl_qos] = missing
            C_lvl_idx[C_lvl_qos] = i_47
            C_lvl_qos += 1
        end
    end
    C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
    resize!(C_lvl_ptr, 1 + 1)
    for p = 1:1
        C_lvl_ptr[p + 1] += C_lvl_ptr[p]
    end
    qos_stop = C_lvl_ptr[1 + 1] - 1
    resize!(C_lvl_idx, qos_stop)
    resize!(C_lvl_2_val, qos_stop)
    (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), C_lvl_stop, C_lvl_ptr, C_lvl_idx)),)
end

result
begin
    C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
    C_lvl_ptr = C_lvl.ptr
    C_lvl_idx = C_lvl.idx
    C_lvl_stop = C_lvl.shape
    C_lvl_2 = C_lvl.lvl
    C_lvl_2_val = C_lvl_2.val
    A_lvl = (((ex.bodies[1]).bodies[2]).body.rhs.args[1]).tns.bind.lvl
    A_lvl_stop = A_lvl.shape
    A_lvl_2 = A_lvl.lvl
    A_lvl_2_val = A_lvl_2.val
    B_lvl = (((ex.bodies[1]).bodies[2]).body.rhs.args[2]).tns.bind.lvl
    B_lvl_stop = B_lvl.shape
    B_lvl_2 = B_lvl.lvl
    B_lvl_2_val = B_lvl_2.val
    C_lvl_qos_stop = 0
    Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
    Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
    C_lvl_qos = 0 + 1
    0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
    phase_stop = min(C_lvl_stop, 0)
    if phase_stop >= 1
        for i_6 = 1:phase_stop
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvl_2_val[C_lvl_qos] = missing
            C_lvl_idx[C_lvl_qos] = i_6
            C_lvl_qos += 1
        end
    end
    phase_stop_2 = min(C_lvl_stop, 10, A_lvl_stop)
    if phase_stop_2 >= 1
        for i_8 = 1:phase_stop_2
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            A_lvl_q = (1 - 1) * A_lvl_stop + i_8
            A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
            C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_2
            C_lvl_idx[C_lvl_qos] = i_8
            C_lvl_qos += 1
        end
    end
    phase_start_3 = max(1, 1 + A_lvl_stop)
    phase_stop_3 = min(C_lvl_stop, 10)
    if phase_stop_3 >= phase_start_3
        for i_10 = phase_start_3:phase_stop_3
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvl_2_val[C_lvl_qos] = missing
            C_lvl_idx[C_lvl_qos] = i_10
            C_lvl_qos += 1
        end
    end
    phase_stop_4 = min(C_lvl_stop, 0, 10 + B_lvl_stop)
    if phase_stop_4 >= 11
        for i_12 = 11:phase_stop_4
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            B_lvl_q = (1 - 1) * B_lvl_stop + (-10 + i_12)
            B_lvl_2_val_2 = B_lvl_2_val[B_lvl_q]
            C_lvl_2_val[C_lvl_qos] = B_lvl_2_val_2
            C_lvl_idx[C_lvl_qos] = i_12
            C_lvl_qos += 1
        end
    end
    phase_stop_5 = min(C_lvl_stop, A_lvl_stop, 10 + B_lvl_stop)
    if phase_stop_5 >= 11
        for i_14 = 11:phase_stop_5
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            A_lvl_q = (1 - 1) * A_lvl_stop + i_14
            B_lvl_q = (1 - 1) * B_lvl_stop + (-10 + i_14)
            A_lvl_2_val_3 = A_lvl_2_val[A_lvl_q]
            B_lvl_2_val_3 = B_lvl_2_val[B_lvl_q]
            C_lvl_2_val[C_lvl_qos] = coalesce(A_lvl_2_val_3, B_lvl_2_val_3)
            C_lvl_idx[C_lvl_qos] = i_14
            C_lvl_qos += 1
        end
    end
    phase_start_6 = max(1 + A_lvl_stop, 11)
    phase_stop_6 = min(C_lvl_stop, 10 + B_lvl_stop)
    if phase_stop_6 >= phase_start_6
        for i_16 = phase_start_6:phase_stop_6
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            B_lvl_q = (1 - 1) * B_lvl_stop + (-10 + i_16)
            B_lvl_2_val_4 = B_lvl_2_val[B_lvl_q]
            C_lvl_2_val[C_lvl_qos] = B_lvl_2_val_4
            C_lvl_idx[C_lvl_qos] = i_16
            C_lvl_qos += 1
        end
    end
    phase_start_7 = max(1, 11 + B_lvl_stop)
    phase_stop_7 = min(C_lvl_stop, 0)
    if phase_stop_7 >= phase_start_7
        for i_18 = phase_start_7:phase_stop_7
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvl_2_val[C_lvl_qos] = missing
            C_lvl_idx[C_lvl_qos] = i_18
            C_lvl_qos += 1
        end
    end
    phase_start_8 = max(1, 11 + B_lvl_stop)
    phase_stop_8 = min(C_lvl_stop, A_lvl_stop)
    if phase_stop_8 >= phase_start_8
        for i_20 = phase_start_8:phase_stop_8
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            A_lvl_q = (1 - 1) * A_lvl_stop + i_20
            A_lvl_2_val_4 = A_lvl_2_val[A_lvl_q]
            C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_4
            C_lvl_idx[C_lvl_qos] = i_20
            C_lvl_qos += 1
        end
    end
    phase_start_9 = max(1, 1 + A_lvl_stop, 11 + B_lvl_stop)
    if C_lvl_stop >= phase_start_9
        for i_22 = phase_start_9:C_lvl_stop
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvl_2_val[C_lvl_qos] = missing
            C_lvl_idx[C_lvl_qos] = i_22
            C_lvl_qos += 1
        end
    end
    C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
    resize!(C_lvl_ptr, 1 + 1)
    for p = 1:1
        C_lvl_ptr[p + 1] += C_lvl_ptr[p]
    end
    qos_stop = C_lvl_ptr[1 + 1] - 1
    resize!(C_lvl_idx, qos_stop)
    resize!(C_lvl_2_val, qos_stop)
    (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), C_lvl_stop, C_lvl_ptr, C_lvl_idx)),)
end


index: Test Failed at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:134
  Expression: check_output("index/concat_offset_permit.jl", #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:136 =# @finch_code(begin
            C .= 0
            #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:137 =#
            for i = _
                #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:138 =#
                C[i] = coalesce(A[~i], B[~(i - 10)])
                #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:139 =#
            end
        end))
   Evaluated: check_output("index/concat_offset_permit.jl", begin
        C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
        C_lvl_ptr = C_lvl.ptr
        C_lvl_idx = C_lvl.idx
        C_lvl_stop = C_lvl.shape
        C_lvl_2 = C_lvl.lvl
        C_lvl_2_val = C_lvl_2.val
        A_lvl = (((ex.bodies[1]).bodies[2]).body.rhs.args[1]).tns.bind.lvl
        A_lvl_stop = A_lvl.shape
        A_lvl_2 = A_lvl.lvl
        A_lvl_2_val = A_lvl_2.val
        B_lvl = (((ex.bodies[1]).bodies[2]).body.rhs.args[2]).tns.bind.lvl
        B_lvl_stop = B_lvl.shape
        B_lvl_2 = B_lvl.lvl
        B_lvl_2_val = B_lvl_2.val
        C_lvl_qos_stop = 0
        Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
        Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
        C_lvl_qos = 0 + 1
        0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
        phase_stop = min(C_lvl_stop, 0)
        if phase_stop >= 1
            for i_6 = 1:phase_stop
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                C_lvl_2_val[C_lvl_qos] = missing
                C_lvl_idx[C_lvl_qos] = i_6
                C_lvl_qos += 1
            end
        end
        phase_stop_2 = min(C_lvl_stop, 10, A_lvl_stop)
        if phase_stop_2 >= 1
            for i_8 = 1:phase_stop_2
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                A_lvl_q = (1 - 1) * A_lvl_stop + i_8
                A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
                C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_2
                C_lvl_idx[C_lvl_qos] = i_8
                C_lvl_qos += 1
            end
        end
        phase_start_3 = max(1, 1 + A_lvl_stop)
        phase_stop_3 = min(C_lvl_stop, 10)
        if phase_stop_3 >= phase_start_3
            for i_10 = phase_start_3:phase_stop_3
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                C_lvl_2_val[C_lvl_qos] = missing
                C_lvl_idx[C_lvl_qos] = i_10
                C_lvl_qos += 1
            end
        end
        phase_stop_4 = min(C_lvl_stop, 0, 10 + B_lvl_stop)
        if phase_stop_4 >= 11
            for i_12 = 11:phase_stop_4
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                B_lvl_q = (1 - 1) * B_lvl_stop + (-10 + i_12)
                B_lvl_2_val_2 = B_lvl_2_val[B_lvl_q]
                C_lvl_2_val[C_lvl_qos] = B_lvl_2_val_2
                C_lvl_idx[C_lvl_qos] = i_12
                C_lvl_qos += 1
            end
        end
        phase_stop_5 = min(C_lvl_stop, A_lvl_stop, 10 + B_lvl_stop)
        if phase_stop_5 >= 11
            for i_14 = 11:phase_stop_5
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                A_lvl_q = (1 - 1) * A_lvl_stop + i_14
                B_lvl_q = (1 - 1) * B_lvl_stop + (-10 + i_14)
                A_lvl_2_val_3 = A_lvl_2_val[A_lvl_q]
                B_lvl_2_val_3 = B_lvl_2_val[B_lvl_q]
                C_lvl_2_val[C_lvl_qos] = coalesce(A_lvl_2_val_3, B_lvl_2_val_3)
                C_lvl_idx[C_lvl_qos] = i_14
                C_lvl_qos += 1
            end
        end
        phase_start_6 = max(1 + A_lvl_stop, 11)
        phase_stop_6 = min(C_lvl_stop, 10 + B_lvl_stop)
        if phase_stop_6 >= phase_start_6
            for i_16 = phase_start_6:phase_stop_6
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                B_lvl_q = (1 - 1) * B_lvl_stop + (-10 + i_16)
                B_lvl_2_val_4 = B_lvl_2_val[B_lvl_q]
                C_lvl_2_val[C_lvl_qos] = B_lvl_2_val_4
                C_lvl_idx[C_lvl_qos] = i_16
                C_lvl_qos += 1
            end
        end
        phase_start_7 = max(1, 11 + B_lvl_stop)
        phase_stop_7 = min(C_lvl_stop, 0)
        if phase_stop_7 >= phase_start_7
            for i_18 = phase_start_7:phase_stop_7
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                C_lvl_2_val[C_lvl_qos] = missing
                C_lvl_idx[C_lvl_qos] = i_18
                C_lvl_qos += 1
            end
        end
        phase_start_8 = max(1, 11 + B_lvl_stop)
        phase_stop_8 = min(C_lvl_stop, A_lvl_stop)
        if phase_stop_8 >= phase_start_8
            for i_20 = phase_start_8:phase_stop_8
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                A_lvl_q = (1 - 1) * A_lvl_stop + i_20
                A_lvl_2_val_4 = A_lvl_2_val[A_lvl_q]
                C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_4
                C_lvl_idx[C_lvl_qos] = i_20
                C_lvl_qos += 1
            end
        end
        phase_start_9 = max(1, 1 + A_lvl_stop, 11 + B_lvl_stop)
        if C_lvl_stop >= phase_start_9
            for i_22 = phase_start_9:C_lvl_stop
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                C_lvl_2_val[C_lvl_qos] = missing
                C_lvl_idx[C_lvl_qos] = i_22
                C_lvl_qos += 1
            end
        end
        C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
        resize!(C_lvl_ptr, 1 + 1)
        for p = 1:1
            C_lvl_ptr[p + 1] += C_lvl_ptr[p]
        end
        qos_stop = C_lvl_ptr[1 + 1] - 1
        resize!(C_lvl_idx, qos_stop)
        resize!(C_lvl_2_val, qos_stop)
        (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), C_lvl_stop, C_lvl_ptr, C_lvl_idx)),)
    end)
  Stacktrace:
   [1] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:753 [inlined]
   [2] top-level scope
     @ ~/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:134
   [3] eval(m::Module, e::Any)
     @ Core ./boot.jl:489
   [4] top-level scope
     @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:136
   [5] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [6] macro expansion
     @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:137 [inlined]
disagreement with reference output
reference
begin
    C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
    C_lvl_ptr = C_lvl.ptr
    C_lvl_idx = C_lvl.idx
    C_lvl_2 = C_lvl.lvl
    C_lvl_2_val = C_lvl_2.val
    A_lvl = ((((ex.bodies[1]).bodies[2]).body.body.rhs.args[1]).args[1]).tns.bind.lvl
    A_lvl_ptr = A_lvl.ptr
    A_lvl_idx = A_lvl.idx
    A_lvl_stop = A_lvl.shape
    A_lvl_2 = A_lvl.lvl
    A_lvl_2_val = A_lvl_2.val
    F_lvl = (((ex.bodies[1]).bodies[2]).body.body.rhs.args[3]).tns.bind.lvl
    F_lvl_stop = F_lvl.shape
    F_lvl_2 = F_lvl.lvl
    F_lvl_2_val = F_lvl_2.val
    C_lvl_qos_stop = 0
    Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
    Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
    C_lvl_qos = 0 + 1
    0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
    A_lvl_q = A_lvl_ptr[1]
    A_lvl_q_stop = A_lvl_ptr[1 + 1]
    if A_lvl_q < A_lvl_q_stop
        A_lvl_i1 = A_lvl_idx[A_lvl_q_stop - 1]
    else
        A_lvl_i1 = 0
    end
    phase_stop = min(A_lvl_i1, A_lvl_stop)
    if phase_stop >= 1
        if A_lvl_idx[A_lvl_q] < 1
            A_lvl_q = Finch.scansearch(A_lvl_idx, 1, A_lvl_q, A_lvl_q_stop - 1)
        end
        while true
            A_lvl_i = A_lvl_idx[A_lvl_q]
            if A_lvl_i < phase_stop
                A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                C_lvldirty = false
                v_3 = -A_lvl_i
                phase_start_3 = max(1, -v_3 + -2)
                phase_stop_4 = min(F_lvl_stop, A_lvl_stop + -v_3 + -3)
                if phase_stop_4 >= phase_start_3
                    A_lvl_q_2 = A_lvl_ptr[1]
                    A_lvl_q_stop_2 = A_lvl_ptr[1 + 1]
                    if A_lvl_q_2 < A_lvl_q_stop_2
                        A_lvl_i1_2 = A_lvl_idx[A_lvl_q_stop_2 - 1]
                    else
                        A_lvl_i1_2 = 0
                    end
                    phase_stop_5 = min(phase_stop_4, -v_3 + -3 + A_lvl_i1_2)
                    if phase_stop_5 >= phase_start_3
                        if A_lvl_idx[A_lvl_q_2] < 3 + v_3 + phase_start_3
                            A_lvl_q_2 = Finch.scansearch(A_lvl_idx, 3 + v_3 + phase_start_3, A_lvl_q_2, A_lvl_q_stop_2 - 1)
                        end
                        while true
                            A_lvl_i_2 = A_lvl_idx[A_lvl_q_2]
                            phase_stop_6 = -v_3 + -3 + A_lvl_i_2
                            if phase_stop_6 < phase_stop_5
                                A_lvl_2_val_3 = A_lvl_2_val[A_lvl_q_2]
                                F_lvl_q = (1 - 1) * F_lvl_stop + phase_stop_6
                                F_lvl_2_val_2 = F_lvl_2_val[F_lvl_q]
                                C_lvldirty = true
                                C_lvl_2_val[C_lvl_qos] = (A_lvl_2_val_2 != 0) * F_lvl_2_val_2 * coalesce(A_lvl_2_val_3, 0) + C_lvl_2_val[C_lvl_qos]
                                A_lvl_q_2 += 1
                            else
                                phase_stop_7 = min(phase_stop_5, -v_3 + -3 + A_lvl_i_2)
                                if A_lvl_i_2 == 3 + v_3 + phase_stop_7
                                    A_lvl_2_val_3 = A_lvl_2_val[A_lvl_q_2]
                                    F_lvl_q = (1 - 1) * F_lvl_stop + phase_stop_7
                                    F_lvl_2_val_3 = F_lvl_2_val[F_lvl_q]
                                    C_lvldirty = true
                                    C_lvl_2_val[C_lvl_qos] += (A_lvl_2_val_2 != 0) * F_lvl_2_val_3 * coalesce(A_lvl_2_val_3, 0)
                                    A_lvl_q_2 += 1
                                end
                                break
                            end
                        end
                    end
                end
                if C_lvldirty
                    C_lvl_idx[C_lvl_qos] = A_lvl_i
                    C_lvl_qos += 1
                end
                A_lvl_q += 1
            else
                phase_stop_10 = min(phase_stop, A_lvl_i)
                if A_lvl_i == phase_stop_10
                    A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvldirty = false
                    v_5 = -phase_stop_10
                    phase_start_11 = max(1, -2 + -v_5)
                    phase_stop_12 = min(F_lvl_stop, A_lvl_stop + -3 + -v_5)
                    if phase_stop_12 >= phase_start_11
                        A_lvl_q_3 = A_lvl_ptr[1]
                        A_lvl_q_stop_3 = A_lvl_ptr[1 + 1]
                        if A_lvl_q_3 < A_lvl_q_stop_3
                            A_lvl_i1_3 = A_lvl_idx[A_lvl_q_stop_3 - 1]
                        else
                            A_lvl_i1_3 = 0
                        end
                        phase_stop_13 = min(phase_stop_12, -3 + -v_5 + A_lvl_i1_3)
                        if phase_stop_13 >= phase_start_11
                            if A_lvl_idx[A_lvl_q_3] < 3 + v_5 + phase_start_11
                                A_lvl_q_3 = Finch.scansearch(A_lvl_idx, 3 + v_5 + phase_start_11, A_lvl_q_3, A_lvl_q_stop_3 - 1)
                            end
                            while true
                                A_lvl_i_3 = A_lvl_idx[A_lvl_q_3]
                                phase_stop_14 = -3 + -v_5 + A_lvl_i_3
                                if phase_stop_14 < phase_stop_13
                                    A_lvl_2_val_4 = A_lvl_2_val[A_lvl_q_3]
                                    F_lvl_q_2 = (1 - 1) * F_lvl_stop + phase_stop_14
                                    F_lvl_2_val_4 = F_lvl_2_val[F_lvl_q_2]
                                    C_lvldirty = true
                                    C_lvl_2_val[C_lvl_qos] += (A_lvl_2_val_2 != 0) * F_lvl_2_val_4 * coalesce(A_lvl_2_val_4, 0)
                                    A_lvl_q_3 += 1
                                else
                                    phase_stop_15 = min(phase_stop_13, -3 + -v_5 + A_lvl_i_3)
                                    if A_lvl_i_3 == 3 + v_5 + phase_stop_15
                                        A_lvl_2_val_4 = A_lvl_2_val[A_lvl_q_3]
                                        F_lvl_q_2 = (1 - 1) * F_lvl_stop + phase_stop_15
                                        F_lvl_2_val_5 = F_lvl_2_val[F_lvl_q_2]
                                        C_lvldirty = true
                                        C_lvl_2_val[C_lvl_qos] += (A_lvl_2_val_2 != 0) * F_lvl_2_val_5 * coalesce(A_lvl_2_val_4, 0)
                                        A_lvl_q_3 += 1
                                    end
                                    break
                                end
                            end
                        end
                    end
                    if C_lvldirty
                        C_lvl_idx[C_lvl_qos] = phase_stop_10
                        C_lvl_qos += 1
                    end
                    A_lvl_q += 1
                end
                break
            end
        end
    end
    C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
    resize!(C_lvl_ptr, 1 + 1)
    for p = 1:1
        C_lvl_ptr[p + 1] += C_lvl_ptr[p]
    end
    qos_stop = C_lvl_ptr[1 + 1] - 1
    resize!(C_lvl_idx, qos_stop)
    resize!(C_lvl_2_val, qos_stop)
    (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), A_lvl_stop, C_lvl_ptr, C_lvl_idx)),)
end

result
begin
    C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
    C_lvl_ptr = C_lvl.ptr
    C_lvl_idx = C_lvl.idx
    C_lvl_2 = C_lvl.lvl
    C_lvl_2_val = C_lvl_2.val
    A_lvl = ((((ex.bodies[1]).bodies[2]).body.body.rhs.args[1]).args[1]).tns.bind.lvl
    A_lvl_stop = A_lvl.shape
    A_lvl_2 = A_lvl.lvl
    A_lvl_2_val = A_lvl_2.val
    F_lvl = (((ex.bodies[1]).bodies[2]).body.body.rhs.args[3]).tns.bind.lvl
    F_lvl_stop = F_lvl.shape
    F_lvl_2 = F_lvl.lvl
    F_lvl_2_val = F_lvl_2.val
    C_lvl_qos_stop = 0
    Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
    Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
    C_lvl_qos = 0 + 1
    0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
    for i_5 = 1:A_lvl_stop
        if C_lvl_qos > C_lvl_qos_stop
            C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
            Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
            Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
            Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
        end
        C_lvldirty = false
        A_lvl_q = (1 - 1) * A_lvl_stop + i_5
        A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
        v_2 = -i_5
        phase_start_2 = max(1, -v_2 + -2)
        phase_stop_2 = min(F_lvl_stop, -v_2 + -3 + A_lvl_stop)
        if phase_stop_2 >= phase_start_2
            for j_6 = phase_start_2:phase_stop_2
                A_lvl_q_2 = (1 - 1) * A_lvl_stop + (v_2 + j_6 + 3)
                F_lvl_q = (1 - 1) * F_lvl_stop + j_6
                A_lvl_2_val_3 = A_lvl_2_val[A_lvl_q_2]
                F_lvl_2_val_2 = F_lvl_2_val[F_lvl_q]
                C_lvldirty = true
                C_lvl_2_val[C_lvl_qos] = (A_lvl_2_val_2 != 0) * coalesce(A_lvl_2_val_3, 0) * F_lvl_2_val_2 + C_lvl_2_val[C_lvl_qos]
            end
        end
        if C_lvldirty
            C_lvl_idx[C_lvl_qos] = i_5
            C_lvl_qos += 1
        end
    end
    C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
    resize!(C_lvl_ptr, 1 + 1)
    for p = 1:1
        C_lvl_ptr[p + 1] += C_lvl_ptr[p]
    end
    qos_stop = C_lvl_ptr[1 + 1] - 1
    resize!(C_lvl_idx, qos_stop)
    resize!(C_lvl_2_val, qos_stop)
    (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), A_lvl_stop, C_lvl_ptr, C_lvl_idx)),)
end


index: Test Failed at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:149
  Expression: check_output("index/sparse_conv.jl", #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:151 =# @finch_code(begin
            C .= 0
            #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:152 =#
            for i = _, j = _
                #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:153 =#
                C[i] += (A[i] != 0) * coalesce(A[(j - i) + 3], 0) * F[j]
                #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:154 =#
            end
        end))
   Evaluated: check_output("index/sparse_conv.jl", begin
        C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
        C_lvl_ptr = C_lvl.ptr
        C_lvl_idx = C_lvl.idx
        C_lvl_2 = C_lvl.lvl
        C_lvl_2_val = C_lvl_2.val
        A_lvl = ((((ex.bodies[1]).bodies[2]).body.body.rhs.args[1]).args[1]).tns.bind.lvl
        A_lvl_stop = A_lvl.shape
        A_lvl_2 = A_lvl.lvl
        A_lvl_2_val = A_lvl_2.val
        F_lvl = (((ex.bodies[1]).bodies[2]).body.body.rhs.args[3]).tns.bind.lvl
        F_lvl_stop = F_lvl.shape
        F_lvl_2 = F_lvl.lvl
        F_lvl_2_val = F_lvl_2.val
        C_lvl_qos_stop = 0
        Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
        Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
        C_lvl_qos = 0 + 1
        0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
        for i_5 = 1:A_lvl_stop
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            C_lvldirty = false
            A_lvl_q = (1 - 1) * A_lvl_stop + i_5
            A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
            v_2 = -i_5
            phase_start_2 = max(1, -v_2 + -2)
            phase_stop_2 = min(F_lvl_stop, -v_2 + -3 + A_lvl_stop)
            if phase_stop_2 >= phase_start_2
                for j_6 = phase_start_2:phase_stop_2
                    A_lvl_q_2 = (1 - 1) * A_lvl_stop + (v_2 + j_6 + 3)
                    F_lvl_q = (1 - 1) * F_lvl_stop + j_6
                    A_lvl_2_val_3 = A_lvl_2_val[A_lvl_q_2]
                    F_lvl_2_val_2 = F_lvl_2_val[F_lvl_q]
                    C_lvldirty = true
                    C_lvl_2_val[C_lvl_qos] = (A_lvl_2_val_2 != 0) * coalesce(A_lvl_2_val_3, 0) * F_lvl_2_val_2 + C_lvl_2_val[C_lvl_qos]
                end
            end
            if C_lvldirty
                C_lvl_idx[C_lvl_qos] = i_5
                C_lvl_qos += 1
            end
        end
        C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
        resize!(C_lvl_ptr, 1 + 1)
        for p = 1:1
            C_lvl_ptr[p + 1] += C_lvl_ptr[p]
        end
        qos_stop = C_lvl_ptr[1 + 1] - 1
        resize!(C_lvl_idx, qos_stop)
        resize!(C_lvl_2_val, qos_stop)
        (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), A_lvl_stop, C_lvl_ptr, C_lvl_idx)),)
    end)
  Stacktrace:
   [1] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:753 [inlined]
   [2] top-level scope
     @ ~/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:149
   [3] eval(m::Module, e::Any)
     @ Core ./boot.jl:489
   [4] top-level scope
     @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:136
   [5] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [6] macro expansion
     @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:137 [inlined]
disagreement with reference output
reference
begin
    C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
    C_lvl_ptr = C_lvl.ptr
    C_lvl_idx = C_lvl.idx
    C_lvl_2 = C_lvl.lvl
    C_lvl_2_val = C_lvl_2.val
    A_lvl = ((ex.bodies[1]).bodies[2]).body.rhs.tns.bind.lvl
    A_lvl_ptr = A_lvl.ptr
    A_lvl_idx = A_lvl.idx
    A_lvl_2 = A_lvl.lvl
    A_lvl_2_val = A_lvl_2.val
    C_lvl_qos_stop = 0
    Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
    Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
    C_lvl_qos = 0 + 1
    0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
    A_lvl_q = A_lvl_ptr[1]
    A_lvl_q_stop = A_lvl_ptr[1 + 1]
    if A_lvl_q < A_lvl_q_stop
        A_lvl_i1 = A_lvl_idx[A_lvl_q_stop - 1]
    else
        A_lvl_i1 = 0
    end
    phase_stop = min(3, A_lvl_i1 + -1)
    if phase_stop >= 1
        if A_lvl_idx[A_lvl_q] < 1 + 1
            A_lvl_q = Finch.scansearch(A_lvl_idx, 1 + 1, A_lvl_q, A_lvl_q_stop - 1)
        end
        while true
            A_lvl_i = A_lvl_idx[A_lvl_q]
            phase_stop_2 = -1 + A_lvl_i
            if phase_stop_2 < phase_stop
                A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
                if C_lvl_qos > C_lvl_qos_stop
                    C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                    Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                    Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                    Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                end
                C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_2
                C_lvl_idx[C_lvl_qos] = phase_stop_2
                C_lvl_qos += 1
                A_lvl_q += 1
            else
                phase_stop_3 = min(phase_stop, -1 + A_lvl_i)
                if A_lvl_i == 1 + phase_stop_3
                    A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
                    if C_lvl_qos > C_lvl_qos_stop
                        C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                        Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                        Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                        Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
                    end
                    C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_2
                    C_lvl_idx[C_lvl_qos] = phase_stop_3
                    C_lvl_qos += 1
                    A_lvl_q += 1
                end
                break
            end
        end
    end
    C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
    resize!(C_lvl_ptr, 1 + 1)
    for p = 1:1
        C_lvl_ptr[p + 1] += C_lvl_ptr[p]
    end
    qos_stop = C_lvl_ptr[1 + 1] - 1
    resize!(C_lvl_idx, qos_stop)
    resize!(C_lvl_2_val, qos_stop)
    (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), 3, C_lvl_ptr, C_lvl_idx)),)
end

result
begin
    C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
    C_lvl_ptr = C_lvl.ptr
    C_lvl_idx = C_lvl.idx
    C_lvl_2 = C_lvl.lvl
    C_lvl_2_val = C_lvl_2.val
    A_lvl = ((ex.bodies[1]).bodies[2]).body.rhs.tns.bind.lvl
    A_lvl_stop = A_lvl.shape
    A_lvl_2 = A_lvl.lvl
    A_lvl_2_val = A_lvl_2.val
    C_lvl_qos_stop = 0
    Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
    Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
    C_lvl_qos = 0 + 1
    0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
    for i_4 = 1:3
        if C_lvl_qos > C_lvl_qos_stop
            C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
            Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
            Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
            Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
        end
        A_lvl_q = (1 - 1) * A_lvl_stop + (1 + i_4)
        A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
        C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_2
        C_lvl_idx[C_lvl_qos] = i_4
        C_lvl_qos += 1
    end
    C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
    resize!(C_lvl_ptr, 1 + 1)
    for p = 1:1
        C_lvl_ptr[p + 1] += C_lvl_ptr[p]
    end
    qos_stop = C_lvl_ptr[1 + 1] - 1
    resize!(C_lvl_idx, qos_stop)
    resize!(C_lvl_2_val, qos_stop)
    (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), 3, C_lvl_ptr, C_lvl_idx)),)
end


index: Test Failed at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:173
  Expression: check_output("index/sparse_window.jl", #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:174 =# @finch_code(begin
            C .= 0
            #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:175 =#
            for i = _
                #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:176 =#
                C[i] = A[(2:4)(i)]
                #= /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:177 =#
            end
        end))
   Evaluated: check_output("index/sparse_window.jl", begin
        C_lvl = ((ex.bodies[1]).bodies[1]).tns.bind.lvl
        C_lvl_ptr = C_lvl.ptr
        C_lvl_idx = C_lvl.idx
        C_lvl_2 = C_lvl.lvl
        C_lvl_2_val = C_lvl_2.val
        A_lvl = ((ex.bodies[1]).bodies[2]).body.rhs.tns.bind.lvl
        A_lvl_stop = A_lvl.shape
        A_lvl_2 = A_lvl.lvl
        A_lvl_2_val = A_lvl_2.val
        C_lvl_qos_stop = 0
        Finch.resize_if_smaller!(C_lvl_ptr, 1 + 1)
        Finch.fill_range!(C_lvl_ptr, 0, 1 + 1, 1 + 1)
        C_lvl_qos = 0 + 1
        0 < 1 || throw((Finch.FinchProtocolError)("SparseListLevels cannot be updated multiple times"))
        for i_4 = 1:3
            if C_lvl_qos > C_lvl_qos_stop
                C_lvl_qos_stop = max(C_lvl_qos_stop << 1, 1)
                Finch.resize_if_smaller!(C_lvl_idx, C_lvl_qos_stop)
                Finch.resize_if_smaller!(C_lvl_2_val, C_lvl_qos_stop)
                Finch.fill_range!(C_lvl_2_val, 0.0, C_lvl_qos, C_lvl_qos_stop)
            end
            A_lvl_q = (1 - 1) * A_lvl_stop + (1 + i_4)
            A_lvl_2_val_2 = A_lvl_2_val[A_lvl_q]
            C_lvl_2_val[C_lvl_qos] = A_lvl_2_val_2
            C_lvl_idx[C_lvl_qos] = i_4
            C_lvl_qos += 1
        end
        C_lvl_ptr[1 + 1] += (C_lvl_qos - 0) - 1
        resize!(C_lvl_ptr, 1 + 1)
        for p = 1:1
            C_lvl_ptr[p + 1] += C_lvl_ptr[p]
        end
        qos_stop = C_lvl_ptr[1 + 1] - 1
        resize!(C_lvl_idx, qos_stop)
        resize!(C_lvl_2_val, qos_stop)
        (C = Tensor((SparseListLevel){Int64}(ElementLevel{0.0, Float64, Int64}(C_lvl_2_val), 3, C_lvl_ptr, C_lvl_idx)),)
    end)
  Stacktrace:
   [1] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:753 [inlined]
   [2] top-level scope
     @ ~/.julia/packages/Finch/ucnKV/test/suites/index_tests.jl:173
   [3] eval(m::Module, e::Any)
     @ Core ./boot.jl:489
   [4] top-level scope
     @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:136
   [5] macro expansion
     @ /opt/julia/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
   [6] macro expansion
     @ ~/.julia/packages/Finch/ucnKV/test/runtests.jl:137 [inlined]
[ Info: Running test item: interface_einsum
[ Info: Running test item: interface_asmd
┌ Warning: Performance Warning: non-concordant traversal of A[i_5, i_3, i_4] (hint: most arrays prefer column major or first index fast, run in fast mode to ignore this warning)
└ @ Finch ?:0
┌ Warning: Performance Warning: non-concordant traversal of A[i_5, i_3, i_4] (hint: most arrays prefer column major or first index fast, run in fast mode to ignore this warning)
└ @ Finch ?:0

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

cmd: /opt/julia/bin/julia 45 running 1 of 1

signal (10): User defined signal 1
_ZN4llvm22containsIrreducibleCFGIPKNS_10BasicBlockEKNS_25ReversePostOrderTraversalIPKNS_8FunctionENS_11GraphTraitsIS7_EEEEKNS_8LoopInfoENS8_IS3_EEEEbRT0_RKT1_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm28mayContainIrreducibleControlERKNS_8FunctionEPKNS_8LoopInfoE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN12_GLOBAL__N_18DSEStateC2ERN4llvm8FunctionERNS1_9AAResultsERNS1_9MemorySSAERNS1_13DominatorTreeERNS1_17PostDominatorTreeERKNS1_17TargetLibraryInfoERKNS1_8LoopInfoE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN12_GLOBAL__N_1L19eliminateDeadStoresERN4llvm8FunctionERNS0_9AAResultsERNS0_9MemorySSAERNS0_13DominatorTreeERNS0_17PostDominatorTreeERKNS0_17TargetLibraryInfoERKNS0_8LoopInfoE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
_ZN4llvm7DSEPass3runERNS_8FunctionERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91
_ZN4llvm11PassManagerINS_8FunctionENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91
_ZN4llvm27ModuleToFunctionPassAdaptor3runERNS_6ModuleERNS_15AnalysisManagerIS1_JEEE at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/usr/include/llvm/IR/PassManagerInternal.h:91
_ZN4llvm11PassManagerINS_6ModuleENS_15AnalysisManagerIS1_JEEEJEE3runERS1_RS3_ at /opt/julia/bin/../lib/julia/libLLVM.so.20.1jl (unknown line)
run at /source/src/pipeline.cpp:787
operator() at /source/src/jitlayers.cpp:1516
withModuleDo<(anonymous namespace)::sizedOptimizerT<N>::operator()(llvm::orc::ThreadSafeModule) [with long unsigned int N = 4]::<lambda(llvm::Module&)> > at /source/usr/include/llvm/ExecutionEngine/Orc/ThreadSafeModule.h:136 [inlined]
operator() at /source/src/jitlayers.cpp:1477 [inlined]
operator() at /source/src/jitlayers.cpp:1652 [inlined]
addModule at /source/src/jitlayers.cpp:2110
jl_compile_codeinst_now at /source/src/jitlayers.cpp:685
jl_compile_codeinst_impl at /source/src/jitlayers.cpp:879
jl_compile_method_internal at /source/src/gf.c:3648
_jl_invoke at /source/src/gf.c:4108 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
collect at ./array.jl:833
unknown function (ip: 0x7bf7091f035e) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
translate_rhs at /home/pkgeval/.julia/packages/Finch/ucnKV/src/Galley/ExecutionEngine/execution-engine.jl:159
#translate_rhs##8 at ./none (unknown line) [inlined]
iterate at ./generator.jl:48 [inlined]
collect at ./array.jl:828
unknown function (ip: 0x7bf7091f035e) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
translate_rhs at /home/pkgeval/.julia/packages/Finch/ucnKV/src/Galley/ExecutionEngine/execution-engine.jl:159
unknown function (ip: 0x7bf7091ed0f8) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
execute_query at /home/pkgeval/.julia/packages/Finch/ucnKV/src/Galley/ExecutionEngine/execution-engine.jl:196
get_execute_code at /home/pkgeval/.julia/packages/Finch/ucnKV/src/Galley/ExecutionEngine/execution-engine.jl:269
#galley#311 at /home/pkgeval/.julia/packages/Finch/ucnKV/src/Galley/Galley.jl:164
galley at /home/pkgeval/.julia/packages/Finch/ucnKV/src/Galley/Galley.jl:69
unknown function (ip: 0x7bf7091dbda1) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
GalleyOptimizer at /home/pkgeval/.julia/packages/Finch/ucnKV/src/Galley/FinchCompat/executor.jl:21
unknown function (ip: 0x7bf7091b8f52) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
#logic_executor_code##0 at /home/pkgeval/.julia/packages/Finch/ucnKV/src/scheduler/LogicExecutor.jl:54
#contain#17 at /home/pkgeval/.julia/packages/Finch/ucnKV/src/environment.jl:116
contain at /home/pkgeval/.julia/packages/Finch/ucnKV/src/environment.jl:111 [inlined]
logic_executor_code at /home/pkgeval/.julia/packages/Finch/ucnKV/src/scheduler/LogicExecutor.jl:51
AdaptiveExecutor at /home/pkgeval/.julia/packages/Finch/ucnKV/src/Galley/FinchCompat/executor.jl:147
compute_parse at /home/pkgeval/.julia/packages/Finch/ucnKV/src/interface/lazy.jl:721
#compute#1199 at /home/pkgeval/.julia/packages/Finch/ucnKV/src/interface/lazy.jl:710
unknown function (ip: 0x7bf6ff1aa7af) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
compute at /home/pkgeval/.julia/packages/Finch/ucnKV/src/interface/lazy.jl:709
unknown function (ip: 0x7bf6ff317d42) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
#std#1229 at /home/pkgeval/.julia/packages/Finch/ucnKV/src/interface/eager.jl:310
std at /home/pkgeval/.julia/packages/Finch/ucnKV/src/interface/eager.jl:307
unknown function (ip: 0x7bf6ff2a45a3) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
macro expansion at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/interface_tests.jl:608 [inlined]
macro expansion at /source/usr/share/julia/stdlib/v1.14/Test/src/Test.jl:1961 [inlined]
#42 at /home/pkgeval/.julia/packages/Finch/ucnKV/test/suites/interface_tests.jl:379
with_scheduler at /home/pkgeval/.julia/packages/Finch/ucnKV/src/interface/lazy.jl:692
unknown function (ip: 0x7bf6ff1a96cc) at (unknown file)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2285 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_stmt_value at /source/src/interpreter.c:194 [inlined]
eval_body at /source/src/interpreter.c:679
jl_interpret_toplevel_thunk at /source/src/interpreter.c:884
jl_toplevel_eval_flex at /source/src/toplevel.c:757
ijl_toplevel_eval at /source/src/toplevel.c:769
ijl_toplevel_eval_in at /source/src/toplevel.c:814
eval at ./boot.jl:489
EvalInto at ./boot.jl:494
jfptr_EvalInto_43243.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2285 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_stmt_value at /source/src/interpreter.c:194 [inlined]
eval_body at /source/src/interpreter.c:679
eval_body at /source/src/interpreter.c:550
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
jl_interpret_toplevel_thunk at /source/src/interpreter.c:884
jl_toplevel_eval_flex at /source/src/toplevel.c:757
jl_eval_toplevel_stmts at /source/src/toplevel.c:600
jl_toplevel_eval_flex at /source/src/toplevel.c:698
ijl_toplevel_eval at /source/src/toplevel.c:769
ijl_toplevel_eval_in at /source/src/toplevel.c:814
eval at ./boot.jl:489
include_string at ./loading.jl:3092
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
_include at ./loading.jl:3152
include at ./Base.jl:310
IncludeInto at ./Base.jl:311
jfptr_IncludeInto_41151.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2285 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_stmt_value at /source/src/interpreter.c:194 [inlined]
eval_body at /source/src/interpreter.c:679
eval_body at /source/src/interpreter.c:550
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
eval_body at /source/src/interpreter.c:558
jl_interpret_toplevel_thunk at /source/src/interpreter.c:884
jl_toplevel_eval_flex at /source/src/toplevel.c:757
jl_eval_toplevel_stmts at /source/src/toplevel.c:600
jl_toplevel_eval_flex at /source/src/toplevel.c:698
ijl_toplevel_eval at /source/src/toplevel.c:769
ijl_toplevel_eval_in at /source/src/toplevel.c:814
eval at ./boot.jl:489
include_string at ./loading.jl:3092
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
_include at ./loading.jl:3152
include at ./Base.jl:310
IncludeInto at ./Base.jl:311
jfptr_IncludeInto_41151.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2285 [inlined]
do_call at /source/src/interpreter.c:123
eval_value at /source/src/interpreter.c:243
eval_stmt_value at /source/src/interpreter.c:194 [inlined]
eval_body at /source/src/interpreter.c:679
jl_interpret_toplevel_thunk at /source/src/interpreter.c:884
jl_toplevel_eval_flex at /source/src/toplevel.c:757
jl_eval_toplevel_stmts at /source/src/toplevel.c:600
jl_toplevel_eval_flex at /source/src/toplevel.c:698
ijl_toplevel_eval at /source/src/toplevel.c:769
ijl_toplevel_eval_in at /source/src/toplevel.c:814
eval at ./boot.jl:489
exec_options at ./client.jl:310
_start at ./client.jl:577
jfptr__start_62684.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2285 [inlined]
true_main at /source/src/jlapi.c:971
jl_repl_entrypoint at /source/src/jlapi.c:1138
main at /source/cli/loader_exe.c:58
unknown function (ip: 0x7bf730047249) at /lib/x86_64-linux-gnu/libc.so.6
__libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
unknown function (ip: 0x4010b8) at /workspace/srcdir/glibc-2.17/csu/../sysdeps/x86_64/start.S
unknown function (ip: (nil)) at (unknown file)


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

[ Info: Running test item: interface_issues

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

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

signal (10): User defined signal 1
epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404
uv_run at /workspace/srcdir/libuv/src/unix/core.c:430
ijl_task_get_next at /source/src/scheduler.c:457
wait at ./task.jl:1246
wait_forever at ./task.jl:1168
jfptr_wait_forever_55793.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:4116 [inlined]
ijl_apply_generic at /source/src/gf.c:4313
jl_apply at /source/src/julia.h:2285 [inlined]
start_task at /source/src/task.c:1272
unknown function (ip: (nil)) at (unknown file)


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

┌ Warning: There were no samples collected in one or more groups.
│ This may be due to idle threads, or you may need to run your
│ program longer (perhaps by running it multiple times),
│ or adjust the delay between samples with `Profile.init()`.
└ @ Profile /opt/julia/share/julia/stdlib/v1.14/Profile/src/Profile.jl:1361
Overhead ╎ [+additional indent] Count File:Line  Function
=========================================================
Thread 1 (default) Task 0x000073bf6675df00 Total snapshots: 425. Utilization: 0%
   ╎425 @Base/task.jl:1168  wait_forever()
424╎ 425 @Base/task.jl:1246  wait()


PkgEval terminated after 2751.57s: test duration exceeded the time limit