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tensorflow/c/eager/abstract_function.h
#include "tensorflow/core/platform/intrusive_ptr.h" #include "tensorflow/core/platform/refcount.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:
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tensorflow/c/eager/tape.h
unneeded_gradients, out_gradients, absl::MakeSpan(in_gradients)); if (!persistent_) { trace.backward_function_deleter(trace.backward_function); } if (!s.ok()) { return s; } } else { if (!persistent_) { trace.backward_function_deleter(trace.backward_function);
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SECURITY.md
BMP, GIF, WAV, RAW, RAW\_PADDED, CSV and PROTO formats. All other input formats, including tensorflow-io should be sandboxed if used to process untrusted data. For example, if an attacker were to upload a malicious video file, they could potentially exploit a vulnerability in the TensorFlow code that handles videos, which could allow them to execute arbitrary code on the system running TensorFlow.
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RELEASE.md
and a [python tracer](https://www.tensorflow.org/guide/profiler#events) which allows you to trace python function calls in your model. Usability improvements include better diagnostic messages and [profile options](https://tensorflow.org/guide/profiler#collect_performance_data) to customize the host and device trace verbosity level.
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