}

TEST(CAPI, StatusEnum) {
  EXPECT_EQ(TF_OK, static_cast<TF_Code>(tensorflow::error::OK));
  EXPECT_EQ(TF_CANCELLED, static_cast<TF_Code>(tensorflow::error::CANCELLED));
  EXPECT_EQ(TF_UNKNOWN, static_cast<TF_Code>(tensorflow::error::UNKNOWN));
  EXPECT_EQ(TF_INVALID_ARGUMENT,
            static_cast<TF_Code>(absl::StatusCode::kInvalidArgument));
  EXPECT_EQ(TF_DEADLINE_EXCEEDED,

                                                                    &device)) {
    methods.deallocator(data);
    status->status =
        tensorflow::errors::InvalidArgument(device_name, " unknown device.");
    return nullptr;
  }
  return tensorflow::wrap(new tensorflow::CAPICustomDeviceTensorHandle(
      context, device, *reinterpret_cast<tensorflow::DataType*>(&dtype), data,
      methods));
}

    return;
  }

  if (num_dims == 0) {
    // Output shape is a scalar.
    return;
  }

  // Rank is greater than 0, so fill in the values, if known, and
  // -1 for unknown values.
  for (int i = 0; i < num_dims; ++i) {
    auto dim = ic->Dim(shape, i);
    int64_t value = -1;
    if (ic->ValueKnown(dim)) {
      value = ic->Value(dim);
    }
    dims[i] = value;

    TF_RETURN_IF_ERROR(
        operation->SetOpName(absl::StrCat("_input_", inputs_.size()).c_str()));
    TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype));
    if (!shape.unknown_rank()) {
      TF_RETURN_IF_ERROR(operation->SetAttrShape(
          "shape", reinterpret_cast<int64_t*>(shape.dim_sizes().data()),
          shape.dims()));
    }
    int num_outputs = 1;

    *   `LinearOperator.matmul` now returns a new `LinearOperator`.
*   New ops and improved op functionality
    *   Add a Nearest Neighbor Resize op.
    *   Add an `ignore_unknown` argument to `parse_values` which suppresses
        ValueError for unknown hyperparameter types. Such * Add
        `tf.linalg.matvec` convenience function.
    *   `tf.einsum()`raises `ValueError` for unsupported equations like
        `"ii->"`.

  # Find all one-step dependencies of those tests which are from //tensorflow
  # (since external deps will come from Python-level pip dependencies),
  # excluding dependencies and files that are known to be unneccessary.
  # This creates a list of targets under //tensorflow that are required for
  # TensorFlow python tests.

// is not interested in).
//
// BackwardFunction needs to be a closure which stores intermediate activations
// from the forward computation and calls a vector-jacobian product function
// (also known as adjoint function) to compute, given downstream gradients,
// upstream gradients.
//
// TODO(apassos) provide concrete template instantiations for TFE_TensorHandle

             'compilation to reduce the compilation time?'),
      'Eigen strong inline overridden.', 'Not overriding eigen strong inline, '
      'some compilations could take more than 20 mins.'):
    # Due to a known MSVC compiler issue
    # https://github.com/tensorflow/tensorflow/issues/10521
    # Overriding eigen strong inline speeds up the compiling of
    # conv_grad_ops_3d.cc and conv_ops_3d.cc by 20 minutes,