TF_DeleteGraph(host_graph_);
    TF_DeleteGraph(func_graph_);
    TF_DeleteStatus(s_);
  }

  void Run(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
           TF_Operation* output, int32_t expected_result) {
    Run(inputs, {{output, 0}}, {expected_result});
  }

  // Run the host graph, which now contains a function and check that
  // outputs are as expected.

# Linux x86 so that we can use RBE. Since tests still need to run on the single
# host Arm64 machine, the build becomes too slow (~30 min) to be a presubmit.
# For testing purposes, we want to see the runtime performance of an
# experimental job that is build-only, i.e, we only build the test targets and
# do not run them. By prefixing the configs with "build", we can run both

}

func BenchmarkIndexRuneUnicode(b *testing.B) {
	b.Run("Latin", func(b *testing.B) {
		// Latin is mostly 1, 2, 3 byte runes.
		benchBytes(b, indexSizes, bmIndexRuneUnicode(unicode.Latin, 'é'))
	})
	b.Run("Cyrillic", func(b *testing.B) {
		// Cyrillic is mostly 2 and 3 byte runes.
		benchBytes(b, indexSizes, bmIndexRuneUnicode(unicode.Cyrillic, 'Ꙁ'))
	})
	b.Run("Han", func(b *testing.B) {

		t.Fatalf("errors happened when create users: %v", err)
	}

	t.Run("First", func(t *testing.T) {
		var first User
		if err := DB.Where("name = ?", "find").First(&first).Error; err != nil {
			t.Errorf("errors happened when query first: %v", err)
		} else {
			CheckUser(t, first, users[0])
		}
	})

	t.Run("Last", func(t *testing.T) {
		var last User

    assertThat(map.get(refreshKey)).isSameInstanceAs(refreshKey);

    new Thread() {
      @Override
      public void run() {
        cache.getUnchecked(getKey);
        getFinishedSignal.countDown();
      }
    }.start();
    new Thread() {
      @Override
      public void run() {
        cache.refresh(refreshKey);
        getFinishedSignal.countDown();
      }
    }.start();

    assertThat(map.get(refreshKey)).isSameInstanceAs(refreshKey);

    new Thread() {
      @Override
      public void run() {
        cache.getUnchecked(getKey);
        getFinishedSignal.countDown();
      }
    }.start();
    new Thread() {
      @Override
      public void run() {
        cache.refresh(refreshKey);
        getFinishedSignal.countDown();
      }
    }.start();

            } else if (TEARDOWN.equals(step.name)) {
                // TEARDOWN should always run to ensure proper cleanup and error handling
                // We'll handle success/failure reporting inside the TEARDOWN phase
                shouldExecute = true;
            } else {
                // For regular steps:
                // Don't run for halted builds, blacklisted projects, or if predecessors failed

        </JSON>
    </OutputSerialization>
    <RequestProgress>
        <Enabled>FALSE</Enabled>
    </RequestProgress>
</SelectObjectContentRequest>`

	for _, testCase := range testTable {
		t.Run(testCase.name, func(t *testing.T) {
			// Hack cpuid to the CPU doesn't appear to support AVX2.
			// Restore whatever happens.
			if cpuid.CPU.Supports(cpuid.AVX2) {
				cpuid.CPU.Disable(cpuid.AVX2)

     */
    public ListenableFuture<?> run(Runnable combiner, Executor executor) {
      return call(
          new Callable<@Nullable Void>() {
            @Override
            public @Nullable Void call() throws Exception {
              combiner.run();
              return null;
            }
          },
          executor);
    }
  }

  // Create a session for this graph.
  CSession csession(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Run the graph.
  csession.SetInputs({{feed, Int32Tensor(3)}});
  csession.SetOutputs({add});
  csession.Run(s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_Tensor* out = csession.output_tensor(0);
  ASSERT_TRUE(out != nullptr);