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tensorflow/c/eager/c_api_unified_experimental.h
// A TF_AbstractOp is the metadata we need to execute an operation. E.g. this // could contain the op type and other attributes. typedef struct TF_AbstractOp TF_AbstractOp; // Stores a function representation that can be used for execution or for // setting functional attributes of other composite ops e.g. control flow. typedef struct TF_AbstractFunction TF_AbstractFunction;
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tensorflow/c/eager/c_api_experimental.h
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tensorflow/c/eager/abstract_context.h
// Operations creation within a context can only be executed in that context // (for now at least). // Implementations of the context may contain some state e.g. an execution // environment, a traced representation etc. class AbstractContext { protected: enum AbstractContextKind { kGraph, kMlir, kEager, kTfrt, kTape, kOpHandler }; explicit AbstractContext(AbstractContextKind kind) : kind_(kind) {}
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Sun Oct 24 11:16:58 GMT 2021 - 3K bytes - Viewed (0) -
tensorflow/c/c_api.h
// non-NULL, in which case it must point to a `TF_Buffer` containing the // serialized representation of a `RunOptions` protocol buffer. // - `run_metadata` may be NULL, in which case it will be ignored; or // non-NULL, in which case it must point to an empty, freshly allocated // `TF_Buffer` that may be updated to contain the serialized representation // of a `RunMetadata` protocol buffer. //
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tensorflow/c/eager/abstract_function.h
#include "tensorflow/core/platform/status.h" namespace tensorflow { class FunctionRecord; // A traced function: this hides the complexity of converting the serialized // representation between various supported formats e.g. FunctionDef and Mlir // function. class AbstractFunction : public core::RefCounted { protected: enum AbstractFunctionKind { kGraph, kMlir };
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tensorflow/c/eager/c_api.h
TFE_TensorDebugInfo* debug_info); // Returns the number of elements in dimension `dim_index`. // Tensor representation on device can be transposed from its representation // on host. The data contained in dimension `dim_index` on device // can correspond to the data contained in another dimension in on-host // representation. The dimensions are indexed using the standard TensorFlow // major-to-minor order (slowest varying dimension first),
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