::tensorflow::Set_TF_Status_from_Status(C_STATUS, _s);      \
      TF_OpKernelContext_Failure(CTX, C_STATUS);                  \
      TF_DeleteStatus(C_STATUS);                                  \
      return;                                                     \
    }                                                             \
  } while (0)

#define TF_CLEANUP_AND_RETURN_IF_ERROR(C_STATUS, BUFFER, __VA_ARGS__) \

                                                   TF_Status* status);

// Returns the status for the tensor handle. In TFRT, a tensor handle can carry
// error info if error happens. If so, the status will be set with the error
// info. If not, status will be set as OK.
TF_CAPI_EXPORT extern void TFE_TensorHandleGetStatus(TFE_TensorHandle* h,
                                                     TF_Status* status);

// here.
void TF_SetTracingImplementation(const char* name, TF_Status*);

// Creates a new TensorFlow function. A Function is an execution context, and as
// such it can trace operations through TF_ExecuteOperation. After completing
// tracing, a function can be obtained by TF_FinalizeFunction.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* status);

// Creates a context for eager execution of operations.

                       const ComputeFunc& compute_func, TF_Status* status) {
    if (max_age_ == 0) {
      return compute_func(key, value, status);
    }

    // Note: we hold onto mu_ for the rest of this function. In practice, this
    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per

  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  virtual Status InputLength(const char* input_name, int* length) = 0;
  virtual Status OutputLength(const char* output_name, int* length) = 0;

  // Set stack trace to be used for potential async error reporting.
  virtual void SetStackTrace(ManagedStackTrace stack_trace) = 0;

#ifndef TENSORFLOW_C_EAGER_TRACING_UTILS_H_
#define TENSORFLOW_C_EAGER_TRACING_UTILS_H_

#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {
namespace tracing {
Status MaybeSetOpName(AbstractOperation*, const char* op_name);
}  // namespace tracing
}  // namespace tensorflow

 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
 * hold the numerical gradient data at the end of the function.
 */
Status CalcNumericalGrad(AbstractContext* ctx, Model forward,
                         absl::Span<AbstractTensorHandle* const> inputs,
                         int input_index, bool use_function,

void Append(const TF_WritableFile* file, const char* buffer, size_t n,
            TF_Status* status);
int64_t Tell(const TF_WritableFile* file, TF_Status* status);
void Flush(const TF_WritableFile* file, TF_Status* status);
void Sync(const TF_WritableFile* file, TF_Status* status);
void Close(const TF_WritableFile* file, TF_Status* status);
}  // namespace tf_writable_file

namespace tf_read_only_memory_region {

                          const char* device, TF_Status* status);
  // Dereferences the backing buffer for the variable. Note that since this can
  // fail (it runs operations), it must be called explicitly and the resulting
  // `status` checked.
  void Destroy(TFE_Context* context, TF_Status* status);

  // Reads from the variable.
  TensorHandlePtr Read(TFE_Context* context, TF_Status* status);
  // Assigns a new value to the variable.

                                                TF_Status* status);

// Obtains statistics for the given path. If status is TF_OK, *stats is
// updated, otherwise it is not touched.
TF_CAPI_EXPORT extern void TF_FileStat(const char* filename,
                                       TF_FileStatistics* stats,
                                       TF_Status* status);