}

  auto* gpu_options = config.mutable_gpu_options();
  gpu_options->set_allow_growth(gpu_memory_allow_growth);

  (*config.mutable_device_count())["CPU"] = num_cpu_devices;

  // TODO(b/113217601): This is needed for EagerContext::runner_ to use a
  // threadpool, so that we avoid the possibility of running the runner_ in the

# proto-message: ActionSpecs

# Python rules should not have more than one source file.
action_spec {
  action: CHECK_FILE_COUNT
  file_count_params {
    rule_selector {
      rule_kind_regex: "^.*py(type)?(_strict)?_(binary|library|test).*$"
      generator_function_regex: "^(?!boq_header)$"
    }
    max_source_count: 1
  }

// set to 1, *undeleted_file_count is set to 0 and an appropriate status (e.g.
// TF_NOT_FOUND) is returned.
//
// If dirname and all its descendants were successfully deleted, TF_OK is
// returned and both error counters are set to zero.
//
// Otherwise, while traversing the tree, undeleted_file_count and
// undeleted_dir_count are updated if an entry of the corresponding type could

                    verbose: bool = False):
  """Creates a JUnit-based XML file, with each invocation as a testcase."""
  os.makedirs(os.path.dirname(output_path), exist_ok=True)
  failure_count = 0
  error_count = 0

  date_time = datetime.datetime
  attrib = {'name': 'Bazel Invocations', 'time': '0.0',
            'timestamp': date_time.isoformat(date_time.utcnow())}
  testsuites = ElemTree.Element('testsuites')

  tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1});
  TF_ASSERT_OK_AND_ASSIGN(
      auto pjrt_client,
      xla::GetTfrtCpuClient(/*asynchronous=*/true, /*cpu_device_count=*/1));
  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});
  TF_ASSERT_OK_AND_ASSIGN(
      auto buffer,
      pjrt_client->BufferFromHostBuffer(

                   TF_Message(status));
    }

    if (device_index == 0) {
      first_op_output_count = per_device_output_tensors.rbegin()->size();
    } else {
      if (first_bad_status == nullptr &&
          per_device_output_tensors.rbegin()->size() != first_op_output_count) {
        first_bad_status.reset(TF_NewStatus());
        TF_SetStatus(first_bad_status.get(), TF_INTERNAL,