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ci/official/requirements_updater/README.md
updating requirements for multiple minor versions of Python. It takes in a file with a set of dependencies, and produces a more detailed requirements file for each version, with hashes specified for each dependency required, as well as their sub-dependencies. ### How to update/add requirements By default, the name of the base requirements file is `requirements.in`, but it
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RELEASE.md
* A new environment variable `TF_XLA_DEBUG_OPTIONS_PASSTHROUGH` set to "1" or "true" allows the debug options passed within an XRTCompile op to be passed directly to the XLA compilation backend. If such variable is not set (service side), only a restricted set will be passed through. * Allow the XRTCompile op to return the ProgramShape resulted form the XLA compilation as a second return argument.
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tensorflow/c/eager/tape.h
// a tensor with the result. virtual Gradient* AggregateGradients( gtl::ArraySlice<Gradient*> gradient_tensors) const = 0; // Calls the passed-in backward function. // // `unneeded_gradients` contains sorted list of input indices for which a // gradient is not required. virtual Status CallBackwardFunction(
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tensorflow/c/c_api_experimental.cc
// function. Otherwise return nullptr in result and error Status from the // function. // // If `library_filename` has already been loaded, we return a cached handle. // Device and Kernels/Ops are registered as globals when a library is loaded // for the first time. TF_Library* TF_LoadPluggableDeviceLibrary(const char* library_filename,
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ci/official/containers/linux_arm64/jax.requirements.txt
# JAX requirements, passed into container by defining the ARG # REQUIREMENTS_FILE=jax.requirements.txt setuptools wheel cloudpickle colorama>=0.4.4 matplotlib pillow>=9.1.0 rich absl-py portpicker six opt-einsum auditwheel typing_extensions importlib_metadata>=4.6 numpy==1.26.0;python_version=="3.12" numpy==1.23.4;python_version=="3.11" numpy==1.22.4;python_version<"3.11" scipy==1.11.2;python_version=="3.12"
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tensorflow/c/c_api_function.cc
TF_Status* status) { tensorflow::AttrValue attr_value; if (!attr_value.ParseFromArray(proto, proto_len)) { status->status = InvalidArgument( "Unparseable AttrValue proto passed to " "TF_FunctionSetAttrValueProto"); return; } auto fdef_or = func->record->mutable_fdef(); if (!fdef_or.ok()) { status->status = fdef_or.status(); return; }
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