Status s =
        BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    ctx.reset(ctx_raw);
  }

  AbstractTensorHandlePtr x;
  {
    AbstractTensorHandle* x_raw = nullptr;
    Status s = TestScalarTensorHandle<float, TF_FLOAT>(ctx.get(), 1.0f, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    x.reset(x_raw);
  }

  // Pseudo-code:

# do not run them. By prefixing the configs with "build", we can run both
# `bazel build` and `bazel test` commands with the same config as test configs
# inherit from build.
build:linux_arm64_pycpp_test_filters --test_tag_filters=-no_oss,-no_aarch64,-oss_excluded,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only

  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_OpAddInput(func, var_handle, status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_TensorHandle* retvals[1] = {nullptr};
  int num_retvals = 1;
  TFE_Execute(func, &retvals[0], &num_retvals, status);

  if (inject_error) {
    ASSERT_EQ(TF_INTERNAL, TF_GetCode(status)) << TF_Message(status);
  } else {

  ASSERT_TF_OK(status_);
  ASSERT_EQ(bucket, "bucket");
  ASSERT_EQ(object, "path/to/object");

  ParseGCSPath("gs://bucket/", true, &bucket, &object, status_);
  ASSERT_TF_OK(status_);
  ASSERT_EQ(bucket, "bucket");

  ParseGCSPath("bucket/path/to/object", false, &bucket, &object, status_);
  ASSERT_EQ(TF_GetCode(status_), TF_INVALID_ARGUMENT);

      TFE_NewOp(context.get(), function_name, status.get()), TFE_DeleteOp);
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  TFE_OpSetDevice(op.get(), device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  TFE_OpAddInput(op.get(), parallel_value.get(), status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

                      /*use_function=*/!std::get<2>(GetParam()));
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_EQ(
      "Provided null gradient_function for 'Neg'.\nIf the intent is to treat "
      "this op as non-differentiable consider using RegisterNotDifferentiable "
      "or NotDifferentiableGradientFunction.",
      s.message());
  ASSERT_EQ(nullptr, outputs[0]);
}

  auto status = pjrt::PjrtApi(DEVICE_CPU);
  if (!status.ok()) {
    TF_ASSERT_OK(pjrt::SetPjrtApi(DEVICE_CPU, GetPjrtApi()));
  }
  TF_ASSERT_OK_AND_ASSIGN(auto pjrt_client, xla::GetCApiClient(DEVICE_CPU));
  TF_ASSERT_OK(SetPjRtClientInTFGlobalResourceManager(DEVICE_CPU,
                                                      std::move(pjrt_client)));

  TF_ASSERT_OK_AND_ASSIGN(
      auto pjrt_client_get,

  };

  TF_ASSERT_OK(InitGraphPlugin(plugin_init));
  ASSERT_EQ(PluginGraphOptimizerRegistry::CreateOptimizers(
                std::set<string>{"Success"})
                .size(),
            1);
  ConfigList config = PluginGraphOptimizerRegistry::GetPluginConfigs(
      true, std::set<string>{"Success"});
  ASSERT_EQ(config.toggle_config["remapping"], RewriterConfig::OFF);
}

                         status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  // Create a variable handle (uninitialized to start) placed on the parallel
  // device.
  std::function<void(Variable*)> variable_deleter = [&](Variable* to_delete) {
    to_delete->Destroy(context, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  return result_store_links


def indent_xml(elem, level=0) -> None:
  """Indents and newlines the XML for better output."""
  indent_str = '\n' + level * '  '
  if len(elem):  # pylint: disable=g-explicit-length-test  # `if elem` not valid
    if not elem.text or not elem.text.strip():
      elem.text = indent_str + '  '
    if not elem.tail or not elem.tail.strip():
      elem.tail = indent_str
    for elem in elem: