// Add some test data
        crawler.addUrl("http://example.com/");

        // Perform cleanup - should not throw exception
        crawler.cleanup(sessionId);
    }

    public void test_stop() throws Exception {
        final CrawlerWebServer server = new CrawlerWebServer(7070);
        server.start();

        try {
            final String url = "http://localhost:7070/";

  }

  public void testStart_whileRunning() {
    stopwatch.start();
    assertThrows(IllegalStateException.class, stopwatch::start);
    assertTrue(stopwatch.isRunning());
  }

  public void testStop() {
    stopwatch.start();
    assertSame(stopwatch, stopwatch.stop());
    assertFalse(stopwatch.isRunning());
  }

  public void testStop_new() {
    assertThrows(IllegalStateException.class, stopwatch::stop);

  VerifyFDefEdges(fdef,
                  {{"feed1", "test_loop/Enter:0"},
                   {"test_loop/Enter:output:0", "test_loop/Merge:0"},
                   {"test_loop/Merge:output:0", "test_loop/Switch:0"},
                   {"test_loop/Switch:output_false:0", "test_loop/Exit:0"},
                   {"test_loop/Exit:output:0", "test_loop_exit"}},
                  {}, false);
}

            stream.read();
            assertEquals(9, stream.available());
            stream.read();
            assertEquals(8, stream.available());
        }
    }

    public void test_skip() throws Exception {
        // Create a temporary file with test data
        File tempFile = File.createTempFile("test-", ".tmp");
        try (FileOutputStream fos = new FileOutputStream(tempFile)) {

    ],
)

tf_custom_op_library(
    name = "test_op1.so",
    srcs = ["test_op1.cc"],
)

tf_kernel_library(
    name = "test_op_kernel",
    srcs = ["test_op.cc"],
    deps = [
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
    ],
    alwayslink = 1,
)

tf_cuda_cc_test(
    name = "env_test",
    size = "medium",

        `(batch_size, 1)`. This enables `Model` subclasses to process scalar
        data in their `train_step()`/`test_step()`/`predict_step()` methods. \
        Note that this change may break certain subclassed models. You can
        revert back to the previous behavior by adding upranking yourself in the
        `train_step()`/`test_step()`/`predict_step()` methods, e.g. `if
        x.shape.rank == 1: x = tf.expand_dims(x, axis=-1)`. Functional models as