}

    /**
     * {@link Vector}の新しいインスタンスを作成して返します。
     *
     * @param <E>
     *            {@link Vector}の要素型
     * @return {@link Vector}の新しいインスタンス
     * @see Vector#Vector()
     */
    public static <E> Vector<E> newVector() {
        return new Vector<>();
    }

    /**
     * {@link Vector}の新しいインスタンスを作成して返します。
     *
     * @param <E>

    EXPECT_TRUE(found);
    TF_DeleteBuffer(op_list_buffer);
  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));

    assertEquals(3, (int) enumer.nextElement());
    assertFalse(enumer.hasMoreElements());
  }

  private static Enumeration<Integer> enumerate(int... ints) {
    Vector<Integer> vector = new Vector<>(Ints.asList(ints));
    return vector.elements();
  }

  public void testToString() {
    Iterator<String> iterator = Lists.newArrayList("yam", "bam", "jam", "ham").iterator();

    assertEquals(3, (int) enumer.nextElement());
    assertFalse(enumer.hasMoreElements());
  }

  private static Enumeration<Integer> enumerate(int... ints) {
    Vector<Integer> vector = new Vector<>(Ints.asList(ints));
    return vector.elements();
  }

  public void testToString() {
    Iterator<String> iterator = Lists.newArrayList("yam", "bam", "jam", "ham").iterator();

build:release_arm64_linux --config=linux_arm64
build:release_arm64_linux --crosstool_top="@ml2014_clang_aarch64_config_aarch64//crosstool:toolchain"
build:release_arm64_linux --config=mkl_aarch64_threadpool
build:release_arm64_linux --copt=-flax-vector-conversions
test:release_arm64_linux --flaky_test_attempts=3

# The old gcc linux build options are preserved in the unsupported_*_linux
# configs. If your project fails to build with Clang, you can use these