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ci/official/requirements_updater/README.md
2) In the `WORKSPACE` file, add the new version to the `python_versions` parameter of the `python_register_multi_toolchains` function. 3) In the `BUILD.bazel` file, add a load statement for the new version, e.g. ``` load("@python//3.11:defs.bzl", compile_pip_requirements_3_11 = "compile_pip_requirements") ``` Add a new entry for the loaded `compile_pip_requirements`, e.g.
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tensorflow/api_template_v1.__init__.py
# Import compat before trying to import summary from tensorboard, so that # reexport_tf_summary can get compat from sys.modules. Only needed if using # lazy loading. _current_module.compat.v2 # pylint: disable=pointless-statement # Load tensorflow-io-gcs-filesystem if enabled if (_os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "true" or _os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "1"):
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WORKSPACE
) load("@pypi//:requirements.bzl", "install_deps") install_deps() # Initialize the TensorFlow repository and all dependencies. # # The cascade of load() statements and tf_workspace?() calls works around the # restriction that load() statements need to be at the top of .bzl files. # E.g. we can not retrieve a new repository with http_archive and then load() # a macro from that repository in the same file.
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SECURITY.md
vulnerabilities. ## Security properties of execution modes TensorFlow has several execution modes, with Eager-mode being the default in v2. Eager mode lets users write imperative-style statements that can be easily inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference
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