Segmentation fault with threaded Distributed

**Affects:** PythonCall

**Describe the bug**

This is a very quirky bug. I'm getting a segmentation fault when using python's `gymnasium` package with multiple processes while a Flux model is loaded on the GPU.

Setup:

```julia
]add CondaPkg
]add PythonCall
]add Flux
]add CUDA

using CondaPkg
CondaPkg.add("gymnasium")
CondaPkg.add("swig")
CondaPkg.add("gymnasium-box2d")
CondaPkg.add("gymnasium-other")
```

Run (crash is non-deterministic, try running a few times on a machine with an NVIDIA GPU):
```julia
using Distributed
addprocs(12; env=["CUDA_HARD_MEMORY_LIMIT" => "5%", "CUDA_MEMORY_POOL"=>"none"])
@everywhere begin
using CUDA
using Flux
using CondaPkg
using PythonCall

function initialize_car_racing_env(_)
gym = pyimport("gymnasium")
x = Flux.Dense(512=>512) |> gpu
env = gym.make("CarRacing-v3")
obs, info = env.reset()
env.close()
return 1
end
end

for generation in 1:10_000
if generation % 100 == 0
println("Generation: $generation")
end
pmap(initialize_car_racing_env, 1:12)
end
```

Stack trace:

```
From worker 5:
From worker 5: [35654] signal 11: Segmentation fault
From worker 5: in expression starting at none:0
From worker 5: jl_gc_state_set at /cache/build/builder-demeter6-6/julialang/julia-master/src/julia_threads.h:334 [inlined]
From worker 5: jl_gc_state_set at /cache/build/builder-demeter6-6/julialang/julia-master/src/julia_threads.h:329 [inlined]
From worker 5: jl_gc_state_save_and_set at /cache/build/builder-demeter6-6/julialang/julia-master/src/julia_threads.h:340
From worker 5: throw_internal_altstack at /cache/build/builder-demeter6-6/julialang/julia-master/src/task.c:755 [inlined]
From worker 5: ijl_sig_throw at /cache/build/builder-demeter6-6/julialang/julia-master/src/task.c:800
From worker 5: Allocations: 21901595 (Pool: 21900914; Big: 681); GC: 219
ERROR: Worker 5 terminated.LoadError:
ProcessExitedException(Unhandled Task ERROR: EOFError: read end of file
Stacktrace:
[1] (::Base.var"#wait_locked#832")(s::Sockets.TCPSocket, buf::IOBuffer, nb::Int64)
@ Base ./stream.jl:970
[2] unsafe_read(s::Sockets.TCPSocket, p::Ptr{UInt8}, nb::UInt64)
@ Base ./stream.jl:978
[3] unsafe_read
@ ./io.jl:891 [inlined]
[4] unsafe_read(s::Sockets.TCPSocket, p::Base.RefValue{NTuple{4, Int64}}, n::Int64)
@ Base ./io.jl:890
[5] read!
@ ./io.jl:895 [inlined]
[6] deserialize_hdr_raw
@ ~/.julia/juliaup/julia-1.11.1+0.x64.linux.gnu/share/julia/stdlib/v1.11/Distributed/src/messages.jl:167 [inlined]
[7] message_handler_loop(r_stream::Sockets.TCPSocket, w_stream::Sockets.TCPSocket, incoming::Bool)
@ Distributed ~/.julia/juliaup/julia-1.11.1+0.x64.linux.gnu/sh
```

**Your system**
Please provide detailed information about your system:
- The operating system

`5.15.0-94-generic #104-Ubuntu SMP Tue Jan 9 15:25:40 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux`

- The version of Julia, Python, PythonCall, JuliaCall and any other affected packages

```
[052768ef] CUDA v5.5.2
[992eb4ea] CondaPkg v0.2.24
[587475ba] Flux v0.14.25
[6099a3de] PythonCall v0.9.23 `https://github.com/JuliaPy/PythonCall.jl.git#main`
[02a925ec] cuDNN v1.4.0
```

```
Julia Version 1.11.1
Commit 8f5b7ca12ad (2024-10-16 10:53 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 64 × AMD Ryzen Threadripper PRO 5975WX 32-Cores
WORD_SIZE: 64
LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 64 virtual cores)
Environment:
LD_LIBRARY_PATH =
```

```
CondaPkg Status /home/garbus/.julia/environments/v1.11/CondaPkg.toml
Environment
/home/garbus/.julia/environments/v1.11/.CondaPkg/env
Packages
gymnasium v1.0.0
gymnasium-box2d v1.0.0
gymnasium-other v1.0.0
swig v4.2.1
```

**Additional context**
I'm researching embodied AI and trying to use Julia's distributed capabilities to do so while still evaluating on python environments.

Start with the supplied Julia reproducer using Distributed, PythonCall, gymnasium, Flux, and CUDA, then compare the worker segmentation-fault stack trace with runs where the crash does not occur. The issue provides no file or test entry point; done would require identifying and resolving the cause so repeated distributed environment initialization no longer terminates a worker.

索引モデルが issue の本文から書いたものです。