code: def test_dtypes_are_correct_with_mixed_precision(self):
      tf.keras.mixed_precision.set_global_policy("mixed_float16")
      try:
          em = self.get_model(compression=True, prior_dtype=tf.float64)
          self.assertIsInstance(
              em.prior, uniform_noise.NoisyLogisticMixture
          )
          self.assertEqual(em.bottleneck_dtype, tf.float16)
          self.assertEqual(em.prior.dtype, tf.float64)
          self.assertEqual(em.prior_dtype, tf.float64)
          x, indexes = self.get_samples((2, 5), dtype=tf.float16)
          x_tilde, bits = em(x, indexes)
          bitstring = em.compress(x, indexes)
          x_hat = em.decompress(bitstring, indexes)
          self.assertEqual(x_hat.dtype, tf.float16)
          self.assertAllClose(x, x_hat, rtol=0, atol=0.5)
          self.assertEqual(x_tilde.dtype, tf.float16)
          self.assertAllClose(x, x_tilde, rtol=0, atol=0.5)
          self.assertEqual(bits.dtype, tf.float64)
          self.assertEqual(bits.shape, (2,))
          self.assertAllGreaterEqual(bits, 0.0)
      finally:
          tf.keras.mixed_precision.set_global_policy(
              tf.keras.backend.floatx()
          )
  
imports: from absl.testing import parameterized
  import tensorflow
  import tensorflow_probability
  from tensorflow_compression.python.distributions import uniform_noise
  from tensorflow_compression.python.entropy_models import continuous_indexed
label_desc: Use image node_id 13 for calling the ContinuousIndexedEntropyModelTest obj's underlying member method code with example usage: obj.test_dtypes_are_correct_with_mixed_precision() without return types