// per device in `underlying_devices`. Implicit copies off of the device throw
// an error.
//
// All component tensors must have the same dtype. Currently they must also have
// the same shape, although this requirement may be relaxed in the future.
//
// `device_name` must not name an existing physical or custom device (see

  //
  // Otherwise if all of the tensors have the same shape, returns that via the
  // `shape` output argument. This blocks waiting for async tensors, may return
  // a delayed bad status encountered during async execution, and will return a
  // bad status unless all tensors have the same shape.
  Status Shape(const std::vector<int64_t>** shape) const;

//           `inputs` cannot contain the same tensor twice.
//  noutputs - number of elements in `outputs` array
//  outputs - array of TF_Outputs that specify the outputs of the function.
//            If `noutputs` is zero (the function returns no outputs), `outputs`
//            can be null. `outputs` can contain the same tensor more than once.

#include "tensorflow/core/platform/status.h"

// Macro to verify that the field `struct_size` of STRUCT_OBJ is initialized.
// `struct_size` is used for struct member compatibility check between core TF
// and plug-ins with the same C API minor version. More info here:
// https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api/C_API_versioning_strategy.md
#define TF_VALIDATE_STRUCT_SIZE(STRUCT_NAME, STRUCT_OBJ, SIZE_VALUE_NAME) \

                                                         TF_Status* status);

// Create a new TFE_TensorHandle with the same contents as 'h' but placed
// in the memory of the device name 'device_name'.
// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least

      if (existing != accumulated_gradients_.end()) {
        // This is a somewhat odd case to be in, since it means we have two
        // operations which supposedly both created the same Tensor. It comes up
        // in recompute_grad, where the gradients have the same value. However,
        // only the original gradient is connected to everything else, so we
        // should still use that.
        vspace_.DeleteGradient(forward_grads[i]);

//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.
//   - The number of `input_tensors` should be the same as the number of items
//     in `input_shapes`.
//
// The results are returned in `output_shapes` and
// `output_resource_shapes_and_types`. The caller is responsible for freeing the

};

// An abstract operation describes an operation by its type, name, and
// attributes. It can be "executed" by the context with some input tensors.
// It is allowed to reusing the same abstract operation for multiple execution
// on a given context, with the same or different input tensors.
class TracingOperation : public AbstractOperation {
 protected:
  explicit TracingOperation(AbstractOperationKind kind)
      : AbstractOperation(kind) {}

  // be executed as an op. Return error if the function with the same name
  // already exists.
  virtual Status AddFunctionDef(const FunctionDef& fdef) = 0;

  // Notifies about the function removal.
  virtual Status AddRemoveFunctionNotifier(const string& func,
                                           std::function<void()> notifier) = 0;

  // Same as `AddFunctionDef`, but additionally saves the `stack_traces` under

  ///
  /// Similar to `rename_file`, but both `src` and `dst` exist after this call
  /// with the same contents. In case of error, both `src` and `dst` keep the
  /// same state as before the call.
  ///
  /// If `dst` is a directory, creates a file with the same name as the source
  /// inside the target directory.
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if rename was completed.