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tensorflow/c/c_api.cc
using tensorflow::PartialTensorShape; using tensorflow::RunMetadata; using tensorflow::RunOptions; using tensorflow::Session; using tensorflow::Status; using tensorflow::string; using tensorflow::Tensor; using tensorflow::TensorId; using tensorflow::TensorShapeProto; using tensorflow::VersionDef; using tensorflow::errors::FailedPrecondition; using tensorflow::errors::InvalidArgument; using tensorflow::errors::OutOfRange;
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CONTRIBUTING.md
- In such situations, we may request you to make further changes to your PR for the tests to pass. - Once the tests pass, we now bring all the code into the internal code base, using a job called "copybara". **5. Copy to Google Internal codebase and run internal CI** - Once the PR is in the Google codebase, we make sure it integrates well with its dependencies and the rest of the system.
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tensorflow/c/c_api_experimental.cc
TF_Status* status) { using tensorflow::NodeDef; using tensorflow::OpRegistrationData; using tensorflow::Tensor; using tensorflow::shape_inference::DimensionHandle; using tensorflow::shape_inference::InferenceContext; using tensorflow::shape_inference::ShapeAndType; using tensorflow::shape_inference::ShapeHandle; const int num_inputs = input_shapes->num_items;
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h" #include "tensorflow/core/platform/errors.h" #include "tensorflow/core/platform/strcat.h" #include "tensorflow/core/platform/types.h" using tensorflow::dyn_cast; using tensorflow::string; using tensorflow::gtl::ArraySlice; namespace tensorflow { namespace tracing { namespace graph { class GraphContext; class GraphOperation; class GraphTensor;
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RELEASE.md
* Using `functools.wraps` on a function with different signature * Using `functools.partial` with an invalid `tf.function` input
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tensorflow/c/experimental/filesystem/plugins/posix/BUILD
cc_library( name = "posix_filesystem_helper", srcs = ["posix_filesystem_helper.cc"], hdrs = ["posix_filesystem_helper.h"], deps = [":copy_file"], ) # On Linux, we can copy files faster using `sendfile`. But not elsewhere. # Hence, this private library to select which implementation to use. cc_library( name = "copy_file", srcs = select({ "//tensorflow:linux_x86_64": ["copy_file_linux.cc"],
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.bazelrc
# rbe_win_clang: Options specific to compiling using Clang. # # rbe_linux_cpu: RBE options to build with only CPU support. # rbe_linux_cuda: RBE options to build with GPU support using clang. # rbe_linux_cuda_nvcc: RBE options to build with GPU support using nvcc. # # Embedded Linux options (experimental and only tested with TFLite build yet)
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tensorflow/c/eager/tape.h
// produced this tensor. A value of -1 means that the tensor was directly // watched and not the result of any operation in the tape. using TensorTape = std::unordered_map<int64_t, int64_t>; // Map from operation-id to tape entry. template <typename BackwardFunction, typename TapeTensor> using OpTape = std::unordered_map<int64_t, OpTapeEntry<BackwardFunction, TapeTensor>>;
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configure.py
def get_ndk_api_level(environ_cp, android_ndk_home_path): """Gets the appropriate NDK API level to use for the provided Android NDK path. """ # First check to see if we're using a blessed version of the NDK. properties_path = '%s/source.properties' % android_ndk_home_path if is_windows() or is_cygwin(): properties_path = cygpath(properties_path) with open(properties_path, 'r') as f:
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.github/workflows/update-rbe.yml
# match exactly sha256:<hash> because curl may include a ^M symbol at # the end of the line. # See https://cloud.google.com/architecture/using-container-images#exploring_image_manifests_digests_and_tags echo -n "Trying to map name $1 to tag $2... " digest=$(curl -s --head "https://gcr.io/v2/tensorflow-sigs/build/manifests/$2" | egrep -o "sha256:[[:alnum:]]*")
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