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SECURITY.md
executed. The security impact of skipping those checks should be low, since the attack scenario would require a malicious user to be able to control the model which as stated above is already equivalent to code execution. In any case, the recommendation is not to serve models using Eager mode since it also has performance limitations. ## Multi-Tenant environments
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RELEASE.md
symbolic values. * Code already susceptible to leaking tensors outside of graphs becomes slightly more likely to do so now. * Code that tries directly getting gradients with respect to symbolic Keras inputs/outputs. Use `GradientTape` on the actual Tensors passed to the already-constructed model instead.
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tensorflow/BUILD
# # For example, from Python tf.load_op_library loads a custom op library (via # dlopen() on Linux), the library finds libtensorflow_framework.so (no # filesystem search takes place, since libtensorflow_framework.so has already # been loaded by pywrap_tensorflow) and registers its ops and kernels via # REGISTER_OP and REGISTER_KERNEL_BUILDER (which use symbols from # libtensorflow_framework.so), and pywrap_tensorflow can then use these
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CONTRIBUTING.md
TensorFlow team members will be assigned to review your pull requests. Once the pull requests are approved and pass continuous integration checks, a TensorFlow team member will apply `ready to pull` label to your change. This means we are working on getting your pull request submitted to our internal repository. After the change has been submitted internally, your pull request will be merged automatically on GitHub.
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ci/official/requirements_updater/requirements.in
# TODO(b/304751256): Adjust the numpy pin to a single version, when ready numpy ~= 1.23.5 ; python_version <= "3.11" numpy ~= 1.26.0 ; python_version >= "3.12" wheel ~= 0.41.2 h5py >= 3.10.0 lit ~= 17.0.2 opt_einsum == 3.3.0 astunparse == 1.6.3 dill == 0.3.7 astor == 0.7.1 typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras
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