#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 };

// for a GPU tensor this will return a pointer to GPU memory). The pointer is
// only guaranteed to be valid until TFE_DeleteTensorHandle is called on this
// TensorHandle. Only supports POD data types.
TF_CAPI_EXPORT extern void* TFE_TensorHandleDevicePointer(TFE_TensorHandle*,
                                                          TF_Status*);

// See TF_AddGradientsWithPrefix, which provides a means to specify a custom
// name prefix for operations added to a graph to compute the gradients.
//
// WARNING: This function does not yet support all the gradients that python
// supports. See
// https://www.tensorflow.org/code/tensorflow/cc/gradients/README.md
// for instructions on how to add C++ more gradients.
TF_CAPI_EXPORT void TF_AddGradients(TF_Graph* g, TF_Output* y, int ny,

        plugin_memory_free_(std::move(plugin_memory_free)) {}

  ~ModularFileSystem() override { ops_->cleanup(filesystem_.get()); }

  TF_USE_FILESYSTEM_METHODS_WITH_NO_TRANSACTION_SUPPORT;

  Status NewRandomAccessFile(
      const std::string& fname, TransactionToken* token,
      std::unique_ptr<RandomAccessFile>* result) override;
  Status NewWritableFile(const std::string& fname, TransactionToken* token,

  // Following are helper functions to assist integrating TFRT with current
  // TF eager runtime.
  // TODO(b/172877902): These helper functions are currently used to support
  // PyFuncOp on TFRT, and might be useful for ops that directly use low
  // level TF APIs. Remove/replace the following functions when TFRT native
  // ops are implemented.