#define TP_OPTIMIZER_CONFIGS_STRUCT_SIZE \
  TF_OFFSET_OF_END(TP_OptimizerConfigs, scoped_allocator_optimization)

// Struct for Optimizer. Plugin authors must provide an optimize function.
// Creation and deletion functions are optional.
typedef struct TP_Optimizer {
  size_t struct_size;
  void* ext;  // reserved for future use

  // [Optional]
  // Create function for optimizer.
  void* (*create_func)();

  TFE_DeleteTensorHandle(input2);
  TFE_DeleteTensorHandle(identity_ret[0]);
  TFE_DeleteTensorHandle(identity_ret[1]);

  TFE_DeleteOp(cloned_identity);
  TFE_DeleteOp(original_identity);
  TFE_DeleteOp(original_var_op);
  TFE_DeleteOp(cloned);
  TF_DeleteStatus(status);
  TFE_DeleteContext(ctx);
}

TEST(CAPI, ShareVariableAcrossContextsWorks) {

  /// This is accomplished by traversing directory tree rooted at `path` and
  /// deleting entries as they are encountered, from leaves to root. Each plugin
  /// is free to choose a different approach which obtains similar results.
  ///
  /// On successful deletion, `status` must be `TF_OK` and `*undeleted_files`
  /// and `*undeleted_dirs` must be 0. On unsuccessful deletion, `status` must

    std::vector<TFE_TensorHandle*> result;
    result.push_back(nullptr);
    int num_retvals = 1;
    TFE_Execute(op, result.data(), &num_retvals, status);
    TFE_DeleteOp(op);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    ASSERT_EQ(num_retvals, 1);

    TF_Tensor* r = TFE_TensorHandleResolve(result[0], status);

typedef struct TFE_TensorDebugInfo TFE_TensorDebugInfo;

// Retrieves TFE_TensorDebugInfo for `handle`.
// If TFE_TensorHandleTensorDebugInfo succeeds, `status` is set to OK and caller
// is responsible for deleting returned TFE_TensorDebugInfo.
// If TFE_TensorHandleTensorDebugInfo fails, `status` is set to appropriate
// error and nullptr is returned. This function can block till the operation
// that produces `handle` has completed.

                    /*input_shapes*/ {unknown_shape(), unknown_shape()},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({kUnknownDim, kUnknownDim}));

  TFE_DeleteOp(matmul_op);
  // TODO(bgogul): Add some death tests where status is not OK.
}

TEST_F(ShapeInferenceTest, InfersShapesFromInputTensors) {
  // Prepare some tensors for shape.

  TFE_TensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TFE_Execute(op, &var_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_DeleteOp(op);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  CHECK_EQ(1, num_retvals);

  // Assign 'value' to it.
  op = TFE_NewOp(ctx, "AssignVariableOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.
  void DeleteTrace(const AbstractTensorHandle*);

  // Consumes the internal state of the tape (so cannot be called more than

  status->status = new_op->Reset(op_or_function_name, nullptr);
  if (!status->status.ok()) {
    new_op->Release();
    new_op = nullptr;
  }
  return tensorflow::wrap(new_op);
}

void TFE_DeleteOp(TFE_Op* op) {
  if (op == nullptr) {
    return;
  }

  tensorflow::unwrap(op)->Release();
}

const char* TFE_OpGetName(const TFE_Op* op, TF_Status* status) {

    `tf.train.experimental.enable_mixed_precision_graph_rewrite()`.
*   Add environment variable `TF_CUDNN_DETERMINISTIC`. Setting to "true" or "1"
    forces the selection of deterministic cuDNN convolution and max-pooling
    algorithms. When this is enabled, the algorithm selection procedure itself
    is also deterministic.

## Breaking Changes

*   Many backwards incompatible API changes have been made to clean up the APIs