"/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_pass_count");

  auto status = BuildAndRunPipeline(kMlirIllegalOpStr, legalizeTFPasses());

  EXPECT_TRUE(status);
  EXPECT_EQ(legalize_failure_count.Read("tf.DoesntExist", "Unknown"), 1);
}

TEST(XlaLegalizeTest, LegalOp) {
  // We expect legalization to fail for legal op with dynamic shapes:
  static constexpr char kMlirLegalOpStr[] = R"(

  EXPECT_EQ(Explain(add.node(), NodeWith(Inputs())),
            "\nexpected 0 inputs but node has 2");
  EXPECT_EQ(
      Explain(add.node(), NodeWith(Inputs(Out(NodeWith(Name("blah"))), _))),
      "\ninput 0 does not match expected:\nname: blah, \nsource does not match "
      "expected name: blah\n\t\nexpected name blah but found placeholder_a");
  EXPECT_EQ(

  string upper_snake;
};

void TestSingleVariation(const string &str, Variations expected,
                         char delimiter = '_') {
  EXPECT_EQ(expected.lower_camel, toLowerCamel(str, delimiter));
  EXPECT_EQ(expected.lower_snake, toLowerSnake(str, delimiter));
  EXPECT_EQ(expected.upper_camel, toUpperCamel(str, delimiter));
  EXPECT_EQ(expected.upper_snake, toUpperSnake(str, delimiter));
}

	expectInt(t, completions.Load, 2)

	close(stop)
}

func expectInt(t *testing.T, f func() int32, expected int32) {
	t.Helper()
	retry.UntilSuccessOrFail(t, func() error {
		got := f()
		if got != expected {
			return fmt.Errorf("unexpected count: %v, want %v", got, expected)
		}
		return nil
	}, retry.Timeout(time.Second))
}

func TestLeaderElectionDisabled(t *testing.T) {

  EXPECT_EQ(tensor.dims(), 1);
  EXPECT_EQ(tensor.dtype(), dtype);
  absl::Span<const typename TypeParam::type> tensor_view(
      reinterpret_cast<typename TypeParam::type*>(tensor.data()), value.size());
  EXPECT_EQ(tensor_view[0], 42);
  EXPECT_EQ(tensor_view[1], 100);
  EXPECT_EQ(tensor_view[2], 0);
  EXPECT_EQ(tensor_view[3], 1);
  EXPECT_EQ(tensor_view[4], 4);

  TF_EXPECT_OK(CompileWithComputation(function_to_hlo_args).status());

  Histogram histogram =
      compilation_time.Delta("graph_old_bridge_has_function_to_hlo");

  EXPECT_EQ(histogram.num(), 1);
  EXPECT_EQ(compilation_status.Delta("kOldBridgeNoMlirSuccess"), 1);
}

TEST_F(CompileTFGraphTest, SuccessfullyCompilesWithManualSharding) {
  // MLIR module from failing test.

void Verify(const RuntimeMetadata* result, const RuntimeMetadata* expected) {
  EXPECT_EQ(result->subgraph_metadata()->size(),
            expected->subgraph_metadata()->size());
  for (int i = 0; i < result->subgraph_metadata()->size(); ++i) {
    auto result_subgraph_metadata =
        result->subgraph_metadata()->GetAs<SubgraphMetadata>(i);
    auto expected_subgraph_metadata =
        expected->subgraph_metadata()->GetAs<SubgraphMetadata>(i);

  ASSERT_TRUE(out != nullptr);
  EXPECT_EQ(TF_FLOAT, TF_TensorType(out));
  EXPECT_EQ(2, TF_NumDims(out));
  EXPECT_EQ(4, TF_Dim(out, 0));
  EXPECT_EQ(1, TF_Dim(out, 1));
  float* values = static_cast<float*>(TF_TensorData(out));
  // These values are defined to be (input / 2) + 2.
  EXPECT_EQ(2, values[0]);
  EXPECT_EQ(2.5, values[1]);
  EXPECT_EQ(3, values[2]);
  EXPECT_EQ(3.5, values[3]);

                           &shape_info));
  auto iter = shape_info.find("sink");
  EXPECT_NE(iter, shape_info.end());
  EXPECT_EQ(iter->second.size(), 1);
  EXPECT_EQ(iter->second.at(0).handle_type, DT_FLOAT);
  TensorShape resource_shape;
  EXPECT_TRUE(iter->second.at(0).handle_shape.AsTensorShape(&resource_shape));
  EXPECT_EQ(resource_shape, TensorShape({2, 3}));
}

}  // namespace

		claimStates []string
		expected    bool
		skipPodUID  bool
	}{
		{
			name:        "all missing",
			claimStates: []string{missing, missing},
			expected:    false,
		},
		{
			name:        "no claims",
			claimStates: []string{},
			expected:    false,
		},
		{
			name:        "exists",
			claimStates: []string{missing, exists},
			expected:    false,
		},
		{