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tensorflow/c/c_api.cc
} namespace { // Helper method that creates a shape handle for a shape described by dims. tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims( tensorflow::shape_inference::InferenceContext* ic, int num_dims, const int64_t* dims) { if (num_dims != -1) { std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec; dim_vec.reserve(num_dims); for (int i = 0; i < num_dims; ++i) {
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tensorflow/c/c_api_internal.h
struct TF_Library { void* lib_handle; TF_Buffer op_list; }; struct TF_Graph { TF_Graph(); mutable tensorflow::mutex mu; tensorflow::Graph graph TF_GUARDED_BY(mu); // Runs shape inference. tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu); // Maps from name of an operation to the Node* in 'graph'. std::unordered_map<tensorflow::string, tensorflow::Node*> name_map TF_GUARDED_BY(mu);
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tensorflow/c/c_api_experimental.cc
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; NodeDef node_def;
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tensorflow/c/c_api_experimental.h
// `shape_inference::InferenceContext` constructor. Note the following: // - The inputs of the `op` are not used for shape inference. So, it is // OK to not have the inputs properly set in `op`. See `input_tensors` // if you want shape inference to consider the input tensors of the // op for shape inference. // - The types need not be set in `input_shapes` as it is not used.
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tensorflow/c/c_api_test.cc
.Attr("v: " #type) \ .SetShapeFn(tensorflow::shape_inference::UnknownShape); \ REGISTER_OP("CApiAttributesTestOpList" #type) \ .Attr("v: list(" #type ")") \ .SetShapeFn(tensorflow::shape_inference::UnknownShape) ATTR_TEST_REGISTER_OP(string); ATTR_TEST_REGISTER_OP(int); ATTR_TEST_REGISTER_OP(float);
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
### 2. Code Provide code to help us reproduce your issues using one of the following options: #### Option A: Reference colab notebooks 1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
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CITATION.cff
“parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and...
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tensorflow/c/experimental/filesystem/plugins/gcs/cleanup.h
F release() { released_ = true; return std::move(f_); } bool is_released() const { return released_; } private: static_assert(!std::is_reference<F>::value, "F must not be a reference"); bool released_ = false; F f_; }; template <int&... ExplicitParameterBarrier, typename F, typename DecayF = typename std::decay<F>::type>
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tensorflow/c/eager/graph_function.h
~GraphFunction() override; // GraphFunction maybe stay alive for the duration of the returned // FunctionDef. Status GetFunctionDef(const FunctionDef** fdef) override; // Returns a shared reference to the wrapped function. absl::StatusOr<core::RefCountPtr<FunctionRecord>> GetFunctionRecord() override { return func_record_.GetNewRef(); } // For LLVM style RTTI.
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tensorflow/c/eager/c_api.h
TF_CAPI_EXPORT extern int TFE_OpGetFlatInputCount(const TFE_Op* op, TF_Status* status); // Returns a borrowed reference to one of `op`'s inputs. Use // `TFE_TensorHandleCopySharingTensor` to make a new reference. TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_OpGetFlatInput(const TFE_Op* op, int index,
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