Problem when try to install Executorh in Kaggle

🐛 Describe the bug

when i try to install executorch in Kaggle environment it's displaying error

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
catboost 1.2.7 requires numpy<2.0,>=1.16.0, but you have numpy 2.0.0 which is incompatible.
cupy-cuda12x 12.2.0 requires numpy<1.27,>=1.20, but you have numpy 2.0.0 which is incompatible.
fastai 2.7.18 requires torch<2.6,>=1.10, but you have torch 2.6.0 which is incompatible.
gensim 4.3.3 requires numpy<2.0,>=1.18.5, but you have numpy 2.0.0 which is incompatible.
langchain 0.3.12 requires numpy<2,>=1.22.4; python_version < "3.12", but you have numpy 2.0.0 which is incompatible.
matplotlib 3.7.5 requires numpy<2,>=1.20, but you have numpy 2.0.0 which is incompatible.
mkl-fft 1.3.8 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.0.0 which is incompatible.
mkl-random 1.2.4 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.0.0 which is incompatible.
mkl-umath 0.1.1 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.0.0 which is incompatible.
mlxtend 0.23.3 requires scikit-learn>=1.3.1, but you have scikit-learn 1.2.2 which is incompatible.
pandas-gbq 0.25.0 requires google-api-core<3.0.0dev,>=2.10.2, but you have google-api-core 1.34.1 which is incompatible.
plotnine 0.14.4 requires matplotlib>=3.8.0, but you have matplotlib 3.7.5 which is incompatible.
pylibcugraph-cu12 24.10.0 requires pylibraft-cu12==24.10.*, but you have pylibraft-cu12 24.12.0 which is incompatible.
pylibcugraph-cu12 24.10.0 requires rmm-cu12==24.10.*, but you have rmm-cu12 24.12.1 which is incompatible.
pytensor 2.26.4 requires numpy<2,>=1.17.0, but you have numpy 2.0.0 which is incompatible.
tensorflow 2.17.1 requires numpy<2.0.0,>=1.23.5; python_version <= "3.11", but you have numpy 2.0.0 which is incompatible.
tensorflow-decision-forests 1.10.0 requires tensorflow==2.17.0, but you have tensorflow 2.17.1 which is incompatible.
thinc 8.2.5 requires numpy<2.0.0,>=1.19.0; python_version >= "3.9", but you have numpy 2.0.0 which is incompatible.
Successfully installed execnet-2.1.1 executorch-0.5.0 expecttest-0.3.0 hypothesis-6.124.9 numpy-2.0.0 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-cusparselt-cu12-0.6.2 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.4.127 parameterized-0.9.0 pytest-xdist-3.6.1 ruamel.yaml-0.18.10 ruamel.yaml.clib-0.2.12 torch-2.6.0 torchaudio-2.6.0 torchvision-0.21.0 triton-3.2.0

i try in colab have same error

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
langchain 0.3.16 requires numpy<2,>=1.22.4; python_version < "3.12", but you have numpy 2.0.0 which is incompatible.
gensim 4.3.3 requires numpy<2.0,>=1.18.5, but you have numpy 2.0.0 which is incompatible.
pytensor 2.26.4 requires numpy<2,>=1.17.0, but you have numpy 2.0.0 which is incompatible.
fastai 2.7.18 requires torch<2.6,>=1.10, but you have torch 2.6.0 which is incompatible.
thinc 8.2.5 requires numpy<2.0.0,>=1.19.0; python_version >= "3.9", but you have numpy 2.0.0 which is incompatible.

but when running the executorch library does not cause an error like this that occurs on the kaggle platform:

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-5-03382cd9a8cb> in <cell line: 5>()
      3 from torch import nn
      4 from torch.export import ExportedProgram, export, export_for_training
----> 5 import executorch.exir as exir
      6 from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
      7 from executorch.exir import (

/usr/local/lib/python3.10/dist-packages/executorch/exir/__init__.py in <module>
      7 from typing import Any
      8 
----> 9 from executorch.exir.capture import (
     10     _capture_legacy_do_not_use,
     11     CallSpec,

/usr/local/lib/python3.10/dist-packages/executorch/exir/capture/__init__.py in <module>
      7 # pyre-strict
      8 
----> 9 from executorch.exir.capture._capture import (
     10     _capture_legacy_do_not_use,
     11     CallSpec,

/usr/local/lib/python3.10/dist-packages/executorch/exir/capture/_capture.py in <module>
     13 
     14 import torch
---> 15 from executorch.exir.capture._config import CaptureConfig
     16 from executorch.exir.error import ExportError, ExportErrorType, InternalError
     17 from executorch.exir.program import ExirExportedProgram

/usr/local/lib/python3.10/dist-packages/executorch/exir/capture/_config.py in <module>
     13 from executorch.exir.dynamic_shape import DynamicMemoryPlanningMode
     14 from executorch.exir.pass_manager import PassType
---> 15 from executorch.exir.passes import MemoryPlanningPass, ToOutVarPass
     16 from executorch.exir.passes.sym_shape_eval_pass import ConstraintBasedSymShapeEvalPass
     17 from executorch.exir.tracer import ExirDynamoConfig

/usr/local/lib/python3.10/dist-packages/executorch/exir/passes/__init__.py in <module>
     17 
     18 import torch
---> 19 from executorch.exir import control_flow, memory, memory_planning
     20 from executorch.exir.common import override_logger
     21 from executorch.exir.delegate import executorch_call_delegate

/usr/local/lib/python3.10/dist-packages/executorch/exir/control_flow.py in <module>
     56 import torch.utils._pytree as pytree
     57 from executorch.exir.error import ExportError, ExportErrorType, internal_assert
---> 58 from executorch.exir.tracer import (
     59     DispatchTracer,
     60     flattened_dispatch_trace,

/usr/local/lib/python3.10/dist-packages/executorch/exir/tracer.py in <module>
     48 from torch._dynamo.guards import Guard
     49 from torch._functorch.eager_transforms import _maybe_unwrap_functional_tensor
---> 50 from torch.export import default_decompositions
     51 from torch.func import functionalize
     52 from torch.fx.operator_schemas import normalize_function

ImportError: cannot import name 'default_decompositions' from 'torch.export' (/usr/local/lib/python3.10/dist-packages/torch/export/__init__.py)

At first I thought the problem was installing the package, but when in google colab it worked fine I don't think that was the problem.

Versions

PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.31.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 6 2024, 20:22:13) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.6.56+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla P100-PCIE-16GB
Nvidia driver version: 560.35.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 4
On-line CPU(s) list: 0-3
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.00GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 2
Socket(s): 1
Stepping: 3
BogoMIPS: 4000.38
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 64 KiB (2 instances)
L1i cache: 64 KiB (2 instances)
L2 cache: 2 MiB (2 instances)
L3 cache: 38.5 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; IBRS; IBPB conditional; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT Host state unknown

Versions of relevant libraries:
[pip3] executorch==0.5.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==2.0.0
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] nvtx==0.2.10
[pip3] onnx==1.17.0
[pip3] optree==0.13.1
[pip3] pynvjitlink-cu12==0.4.0
[pip3] pytorch-ignite==0.5.1
[pip3] pytorch-lightning==2.5.0.post0
[pip3] torch==2.6.0
[pip3] torchaudio==2.6.0
[pip3] torchinfo==1.8.0
[pip3] torchmetrics==1.6.1
[pip3] torchsummary==1.5.1
[pip3] torchtune==0.5.0
[pip3] torchvision==0.21.0
[pip3] triton==3.2.0
[conda] Could not collect

Start by comparing the Kaggle and Colab installation environments and reproducing the import of executorch.exir, focusing on the reported torch.export.default_decompositions traceback. Check the listed executorch, PyTorch, NumPy, Python, and CUDA versions; done means the supported environment or compatibility problem is clearly identified and the installation/import behavior is corrected or documented.