Entry<K, V> entry = iterator.next();
      Entry<K, V> entryCopy = mapEntry(entry.getKey(), entry.getValue());

      iterator.remove();
      assertEquals(initialSize - 1, map.size());

      // Use "entryCopy" instead of "entry" because "entry" might be invalidated after
      // iterator.remove().
      assertFalse(entrySet.contains(entryCopy));
      assertInvariants(map);

      Entry<K, V> entry = iterator.next();
      Entry<K, V> entryCopy = mapEntry(entry.getKey(), entry.getValue());

      iterator.remove();
      assertEquals(initialSize - 1, map.size());

      // Use "entryCopy" instead of "entry" because "entry" might be invalidated after
      // iterator.remove().
      assertFalse(entrySet.contains(entryCopy));
      assertInvariants(map);

      -1

      """.trimIndent()
    val entryBody = "abc"
    val journalBody = """libcore.io.DiskLruCache
1
201105
2

CLEAN $urlKey ${entryMetadata.length} ${entryBody.length}
"""
    fileSystem.createDirectory(cache.directoryPath)
    writeFile(cache.directoryPath, "$urlKey.0", entryMetadata)
    writeFile(cache.directoryPath, "$urlKey.1", entryBody)

        `tf.initializers = tf.keras.initializers` & `tf.optimizers =
        tf.keras.optimizers`.
    *   Updates binary cross entropy logic in Keras when input is probabilities.
        Instead of converting probabilities to logits, we are using the cross
        entropy formula for probabilities.
    *   Added public APIs for `cumsum` and `cumprod` keras backend functions.