return nullptr;
  }

  size_t total_bytes = dl_tensor->dtype.bits / 8;
  for (int i = 0; i < num_dims; i++) {
    total_bytes *= dims[i];
  }

  if (dl_tensor->strides != nullptr &&
      !IsValidStrideCompactRowMajorData(dl_tensor->shape, dl_tensor->strides,
                                        num_dims)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Invalid strides array from DLPack");

  ASSERT_EQ(errors::OK, status_.code()) << status_.message();
  EXPECT_EQ(outputs[0], nullptr);
  ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[1], {1.0f}, /*dims*/ {},
                                           /*abs_error*/ 0));
  outputs[1]->Unref();
}

#ifdef PLATFORM_GOOGLE
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,

  AbstractTensorHandlePtr sum_dims;
  {
    vector<int32_t> vals(num_dims_out);
    int64_t vals_shape[] = {num_dims_out};
    Range(&vals, 0, num_dims_out);
    AbstractTensorHandle* sum_dims_raw = nullptr;
    TF_RETURN_IF_ERROR(TestTensorHandleWithDims<int32_t, TF_INT32>(
        ctx, vals.data(), vals_shape, 1, &sum_dims_raw));
    sum_dims.reset(sum_dims_raw);
  }

                           const int64_t* dims, const int num_dims,
                           const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op.get(), "dtype", type);
  TFE_OpSetAttrShape(op.get(), "shape", dims, num_dims, status);

      PartialTensorShape component_shape;
      TF_RETURN_IF_ERROR(unwrap(component.get())->Shape(&component_shape));
      if (combined_shape.dims() < 0 ||
          combined_shape.dims() != component_shape.dims()) {
        PartialTensorShape first_shape;
        TF_RETURN_IF_ERROR(unwrap(tensors_[0].get())->Shape(&first_shape));
        return errors::Unimplemented(absl::StrCat(

    std::unique_ptr<LoggedTensor> t, TF_Status* status) {
  auto dtype = TFE_TensorHandleDataType(t->tensor);
  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &LoggedTensorNumDims;
  handle_methods.dim = &LoggedTensorDim;
  handle_methods.deallocator = &LoggedTensorDeallocator;
  return TFE_NewCustomDeviceTensorHandle(context, logging_device_name.c_str(),

  // deleted, it will call ParallelTensorDeallocator to free the struct.
  ParallelTensor* t_released = t.release();
  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &ParallelTensorNumDims;
  handle_methods.dim = &ParallelTensorDim;
  handle_methods.deallocator = &ParallelTensorDeallocator;
  handle_methods.summarize = &ParallelTensorSummarize;

  constexpr int64_t dims[] = {100, 100};
  constexpr int num_elements = dims[0] * dims[1];
  float data[num_elements];
  for (int i = 0; i < num_elements; ++i) {
    data[i] = 1.0f;
  }
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_FLOAT, &dims[0],
                                        sizeof(dims) / sizeof(int64_t), status);

    DCHECK(shape != nullptr);
    TF_Status status;
    int num_dims = TF_GraphGetTensorNumDims(graph_, output_, &status);
    DCHECK_GE(num_dims, -1);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(&status));
    if (num_dims == kUnknownRank) {
      return absl::OkStatus();
    }

    std::vector<int64_t> dims(num_dims, kUnknownDim);
    TF_GraphGetTensorShape(graph_, output_,

    int num_dims = output_shapes->items[0].num_dims;
    int64_t* dims = output_shapes->items[0].dims;

    if (!expected_shape.has_value()) {
      EXPECT_EQ(num_dims, -1);
      EXPECT_EQ(dims, nullptr);
      return;
    }

    EXPECT_EQ(num_dims, expected_shape->size());
    for (size_t i = 0; i < num_dims; ++i) {
      EXPECT_EQ(dims[i], (*expected_shape)[i]);
    }