UnmodifiableIterator<T> iterator) {
    return checkNotNull(iterator);
  }

  /**
   * Returns the number of elements remaining in {@code iterator}. The iterator will be left
   * exhausted: its {@code hasNext()} method will return {@code false}.
   */
  public static int size(Iterator<?> iterator) {
    long count = 0L;
    while (iterator.hasNext()) {
      iterator.next();
      count++;
    }

      assertTrue(
          "index " + i + " expected <" + expected + "., actual is exhausted",
          actualIterator.hasNext());

      Object actual = actualIterator.next();
      assertEquals("index " + i, expected, actual);
      i++;
    }
    if (actualIterator.hasNext()) {
      fail("index " + i + ", expected is exhausted, actual <" + actualIterator.next() + ">");
    }
  }

  /**

      assertTrue(
          "index " + i + " expected <" + expected + "., actual is exhausted",
          actualIterator.hasNext());

      Object actual = actualIterator.next();
      assertEquals("index " + i, expected, actual);
      i++;
    }
    if (actualIterator.hasNext()) {
      fail("index " + i + ", expected is exhausted, actual <" + actualIterator.next() + ">");
    }
  }

  /**

  /**
   * Returns the least of the specified values according to this ordering. If there are multiple
   * least values, the first of those is returned. The iterator will be left exhausted: its {@code
   * hasNext()} method will return {@code false}.
   *
   * <p><b>Java 8+ users:</b> Use {@code Streams.stream(iterator).min(thisComparator).get()} instead

  /**
   * Returns the least of the specified values according to this ordering. If there are multiple
   * least values, the first of those is returned. The iterator will be left exhausted: its {@code
   * hasNext()} method will return {@code false}.
   *
   * <p><b>Java 8+ users:</b> Use {@code Streams.stream(iterator).min(thisComparator).get()} instead

        sampling if it encounters an empty dataset. This preserves the sampling
        ratio throughout training. The prior behavior was to continue sampling,
        skipping over exhausted datasets, until all datasets are exhausted. By
        default, the original behavior (`stop_on_empty_dataset=False`) is
        preserved.
    *   Removed previously deprecated tf.data statistics related APIs: