tf.constant(0, dtype=dtype))]

  expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
  tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)

  expected = tf.cast([not_0] * 4, tf.float32)
  tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)

  # For unsigned dtypes let's also check the result directly.
  if dtype.is_unsigned:

*   `tf.lite`:

    *   Add experimental API `experimental_from_jax` to support conversion from
        Jax models to TensorFlow Lite.
    *   Support uint32 data type for cast op.
    *   Support int8 data type for cast op.
    *   Add experimental quantization debugger `tf.lite.QuantizationDebugger`
    *   Add lite.experimental.authoring.compatible API