Test Failures in the Latest Release (v3.8.0) Test Suite

[ARforyou]

Summary
While attempting to install Keras in editable mode and execute its test suite, several tests failed. Below is a detailed account of the steps taken and the errors encountered.

Steps to Reproduce
Created a Conda environment:

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conda create -n keras python=3.10
conda activate keras
Cloned the Keras repository:

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git clone https://github.com/keras-team/keras.git
cd keras
Checked out the latest release:

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git fetch --tags
git checkout tags/v3.8.0
Installed the requirements:

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python3.10 -m pip install -r requirements.txt
./shell/api_gen.sh
Installed Keras in editable mode:

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python3.10 -m pip install -e .
Configured the backend:

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export KERAS_BACKEND="tensorflow"
Ran the test suite:

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python3.10 -u -m pytest --cov=keras --cov-report=html:htmlcov --cov-report=xml:coverage.xml > keras_golden_run
Observed Behavior
The test suite reported the following summary of errors:

Integration Test Failures
jax_custom_fit_test.py::test_custom_fit

Error: TypeError
CustomModel.train_step() missing 1 required positional argument: 'data'
tf_distribute_training_test.py::test_model_fit

Error: RuntimeError
Virtual devices cannot be modified after being initialized
torch_custom_fit_test.py::test_custom_fit

Error: AttributeError
'CustomModel' object has no attribute 'zero_grad'
torch_workflow_test.py::TorchWorkflowTest::test_keras_layer_in_nn_module

Error: AssertionError
0 != 2
Application Test Failures (EfficientNetV2)
Several tests failed with InvalidArgumentError, involving the inability to broadcast tensor shapes:

Examples:
applications_test.py::test_application_notop_custom_input_shape_EfficientNetV2B0_channels_first
applications_test.py::test_application_pooling_EfficientNetV2B3_channels_first
Error Message:
Unable to broadcast tensor of shape [3] to tensor of shape [1,3,1,1] [Op:BroadcastTo]
Summary of Test Results
12 failed, 12870 passed, 346 skipped, 8 xfailed, 2 xpassed
Total time: ~21 minutes
Environment Details
Python Version: 3.10
Anaconda Version: 4.10.1
OS: Ubuntu 22.04.5 LTS
Additional Notes
Editable Mode: Used pip install -e . instead of pip_build.py to install Keras.
Potential Causes:
Integration test failures may indicate issues with the Jax and PyTorch configurations.
Application test errors might involve TensorFlow device setups or input shape mismatches for EfficientNetV2 models.
Request for Assistance
I expected the test suite to pass or, at the very least, have no critical errors.

Are these test failures indicative of underlying issues that require resolution?
Should I reconfigure the backend to include Jax and PyTorch for these tests?
Are additional dependencies or configurations needed for a successful test run?
If TensorFlow is my primary backend, can I safely ignore failures related to Jax and PyTorch?
Any guidance on addressing these issues would be greatly appreciated.

Thank you!