.NewSubScope(child_scope_name);

    if (dx != nullptr) {
      std::vector<tensorflow::Output> dx_arg = OutputsFromTFOutputs(dx, ny);
      status->status =
          AddSymbolicGradients(scope, y_arg, x_arg, dx_arg, &dy_arg);
    } else {
      status->status = AddSymbolicGradients(scope, y_arg, x_arg, &dy_arg);
    }

    // Update g->name_map with the name_map from the scope, which will contain

  if (!status->status.ok()) return nullptr;

  if (input_index >= op_def->input_arg_size() || input_index < 0) {
    status->status = tensorflow::errors::InvalidArgument(
        input_index, " out of range for ", op_name);
    return nullptr;
  }

  const tensorflow::OpDef_ArgDef& input_arg = op_def->input_arg()[input_index];

  if (input_arg.number_attr().empty()) {
    status->status = tensorflow::errors::NotFound(

                                                  TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<tensorflow::OpKernelContext*>(ctx);
  const tensorflow::Tensor& arg_tensor = cc_ctx->input(index);
  absl::Status cc_status;
  if (arg_tensor.dtype() != tensorflow::DT_RESOURCE) {
    cc_status = absl::InvalidArgumentError(
        absl::StrCat("Trying to obtain resource handle from Input[", index,

  CHECK(tensorflow::protobuf::TextFormat::ParseFromString(
      "    signature {"
      "      name: 'AddVariablesFunction'"
      "      input_arg {"
      "        name: 'var'"
      "        type: DT_RESOURCE"
      "      }"
      "      output_arg {"
      "        name: 'sum'"
      "        type: DT_FLOAT"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'read0'"

  // First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);
  TF_Status* status = TF_NewStatus();
  TF_Operation* arg = TF_FinishOperation(arg_descr, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_OperationDescription* id_descr =

    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

    auto cpu_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(hcpu));
    auto gpu_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(hgpu));
    auto gpu_device = gpu_arg->device();
    ASSERT_FALSE(cpu_arg->HasLocalMirror(gpu_device));

    TFE_Op* matmul = MatMulOp(ctx, hcpu, hgpu);
    if (cpu_op) {

  // Build two abstract input tensors as function arguments.
  std::vector<TF_AbstractTensor*> func_args;
  {
    TFE_Context* eager_ctx =
        TF_ExecutionContextGetTFEContext(eager_execution_ctx, s);

    // 1st Arg
    float vals1[] = {0.0f, 1.0f, 1.0f, 0.0f};
    int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
    int num_dims = sizeof(dims) / sizeof(dims[0]);

    TFE_TensorHandle* input_eager =

  EXPECT_EQ(TF_CANCELLED, TF_GetCode(s));
  EXPECT_EQ(string("cancel"), TF_Message(s));
  TF_DeleteStatus(s);
}

void Deallocator(void* data, size_t, void* arg) {
  tensorflow::cpu_allocator()->DeallocateRaw(data);
  *reinterpret_cast<bool*>(arg) = true;
}

TEST(CAPI, Tensor) {
  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(

  if (!status->status.ok()) {
    return nullptr;
  }
  return tensor;
}

// Deleter for DLManagedTensor
void DLManagedTensorDeleter(DLManagedTensor* arg) {
  TfDlManagedTensorCtx* owner =
      static_cast<TfDlManagedTensorCtx*>(arg->manager_ctx);
  owner->reference.Unref();
  delete owner;
}

// Converts TF_DATAType to DLPack data type.
DLDataType GetDlDataType(TF_DataType data_type, TF_Status* status) {

  // Verify that FunctionDef ArgDef has attributes.
  ASSERT_EQ(func_->record->fdef().arg_attr_size(), 1);
  auto arg_attrs = func_->record->fdef().arg_attr().find(0);
  ASSERT_NE(arg_attrs, func_->record->fdef().arg_attr().end());
  auto iter = arg_attrs->second.attr().find("_test_attr");
  ASSERT_NE(iter, arg_attrs->second.attr().end());
  EXPECT_EQ(iter->second.s(), "value");
}