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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
# their dependencies. It's a rewritten version of the "smoke test", an older # Python script that was very difficult to understand. See # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/pip_smoke_test.py @test "Pip package includes all required //tensorflow dependencies" { # grep patterns for packages whose dependencies can be ignored
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CONTRIBUTING.md
* [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1) * [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1) * [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1)
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tensorflow/c/eager/c_api.h
typedef struct TFE_TensorHandle TFE_TensorHandle; TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(const TF_Tensor* t, TF_Status* status); // Indicates that the caller will not be using `h` any more. TF_CAPI_EXPORT extern void TFE_DeleteTensorHandle(TFE_TensorHandle* h); TF_CAPI_EXPORT extern TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h);
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tensorflow/c/eager/c_api_experimental.h
void (*deallocator)(void* data); // Summarizes the value of this tensor. The caller takes ownership of the // returned buffer. If `status` is not TF_OK, instead returns a null pointer. // // Does not include the shape and dtype of the tensor (which is generally // appended later), but should include any information specific to this custom // device which would be useful for debugging. //
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tensorflow/c/experimental/filesystem/modular_filesystem_registration.cc
// * increased security as plugins will not be able to alter function table // after loading up. Thus, malicious plugins can't alter functionality to // probe for gadgets inside core TensorFlow. We can even protect the area // of memory where the copies reside to not allow any more writes to it // after all copies are created. template <typename T>
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tensorflow/c/eager/immediate_execution_context.h
// Configure graph collection in RunMetadata. virtual void SetShouldStoreGraphs(bool value) = 0; // Return the collected RunMetadata. This method will transfer the ownership // to the caller. virtual std::unique_ptr<RunMetadata> ExportRunMetadata() = 0; // For LLVM style RTTI. static bool classof(const AbstractContext* ptr) { return ptr->getKind() == kEager || ptr->getKind() == kTfrt; }
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ci/official/utilities/code_check_full.bats
# their dependencies. It's a rewritten version of the "smoke test", an older # Python script that was very difficult to understand. See # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/pip_smoke_test.py @test "Pip package includes all required //tensorflow dependencies" { # grep patterns for packages whose dependencies can be ignored
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tensorflow/c/eager/tape.h
// // Like GradientTape::RecordOperation, this method takes the operation type // `op_type` (e.g. "Add"), the operation's inputs (`input_tensors`, // `input_tensor_id`, and `input_dtypes`; the latter two are somewhat // redundant but taken as arguments to avoid repeatedly fetching these values // between calls to ShouldRecord and Accumulator), and its outputs // (`output_tensors`). //
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tensorflow/c/c_api_experimental.h
// The returned string is heap-allocated, and caller should call free() on it. TF_CAPI_EXPORT extern const char* TF_GraphDebugString(TF_Graph* graph, size_t* len); // Returns the function content in a human-readable format, with length set in // `len`. The format is subject to change in the future. // The returned string is heap-allocated, and caller should call free() on it. //
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tensorflow/c/c_api_experimental.cc
// scheduled on that same threadpool, causing a deadlock in cases where the // caller of event_mgr->ThenExecute() blocks on the completion of the callback // (as in the case of ConstOp kernel creation on GPU, which involves copying a // CPU tensor to GPU). // Setting a larger thread pool does not help with the Swift caller, as we use // a different TFE context for each thread of execution (for running graph
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