code: def test_dtypes_are_correct_with_mixed_precision(self):
      tf.keras.mixed_precision.set_global_policy("mixed_float16")
      try:
          em = LaplaceEntropyModel(coding_rank=1)
          self.assertEqual(em.bottleneck_dtype, tf.float16)
          x = tf.random.stateless_normal(
              (2, 5), seed=(0, 1), dtype=tf.float16
          )
          x_tilde, penalty = em(x)
          bitstring = em.compress(x)
          x_hat = em.decompress(bitstring, (5,))
          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(penalty.dtype, tf.float16)
          self.assertEqual(penalty.shape, (2,))
      finally:
          tf.keras.mixed_precision.set_global_policy(None)
  
file_path: compression/tensorflow_compression/python/entropy_models/laplace_test.py
imports: import numpy
  import tensorflow
  from tensorflow_compression.python.entropy_models.laplace import LaplaceEntropyModel
label_desc: Use image node_id 9 for calling the LaplaceEntropyModelTest obj's underlying member method code with example usage: obj.test_dtypes_are_correct_with_mixed_precision() without return types