code: def test_actnorm(self):
      _, x_mask, _ = self.get_data()
      x = tf.random_normal(
          (BATCH_SIZE, TARGET_LENGTH, N_CHANNELS),
          mean=50.0,
          stddev=10.0,
          dtype=DTYPE,
      )
      x_act, logabsdet = glow.actnorm(
          "actnorm", x, x_mask, inverse=False, init=True
      )
  
      x_act_nopad = tf.boolean_mask(x_act, x_mask)
      x_mean, x_var = tf.nn.moments(x_act_nopad, axes=[0])
      self.evaluate(tf.global_variables_initializer())
      x, x_act, logabsdet, x_mean, x_var = self.evaluate(
          [x, x_act, logabsdet, x_mean, x_var]
      )
      self.assertEqual(
          x_act.shape, (BATCH_SIZE, TARGET_LENGTH, N_CHANNELS)
      )
      self.assertEqual(logabsdet.shape, (BATCH_SIZE,))
      self.assertTrue(np.allclose(x_mean, 0.0, atol=1e-5))
      self.assertTrue(np.allclose(x_var, 1.0, atol=1e-5))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tempfile
  from absl.testing import parameterized
  import numpy
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import transformer_glow_layers
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models import transformer
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling the TransformerGlowLayersTest obj's underlying member method code with example usage: obj.test_actnorm() without return types