code: def test_conv2d(self):
      with tf.Graph().as_default():
          x = 10.0 * tf.random_uniform(shape=(16, 5, 5, 32))
  
          with arg_scope([glow_ops.actnorm], init=True):
              actnorm_conv2d = glow_ops.conv(
                  "actnorm_conv2d",
                  x,
                  output_channels=64,
                  apply_actnorm=True,
              )
              actnorm_zeros2d = glow_ops.conv(
                  "actnorm_zeros2d",
                  x,
                  output_channels=64,
                  apply_actnorm=False,
              )
  
          with tf.Session() as session:
              session.run(tf.global_variables_initializer())
  
              # test if apply_actnorm is set to True, the first minibatch has
              # zero mean and unit variance.
              actnorm_np, zeros_np = session.run(
                  [actnorm_conv2d, actnorm_zeros2d]
              )
              self.assertEqual(actnorm_np.shape, (16, 5, 5, 64))
              mean = np.mean(actnorm_np, axis=(0, 1, 2))
              var = np.var(actnorm_np, axis=(0, 1, 2))
              self.assertTrue(np.allclose(mean, 0.0, atol=1e-5))
              self.assertTrue(np.allclose(var, 1.0, atol=1e-5))
  
              # test shape in case apply_actnorm is set to False,
              self.assertEqual(zeros_np.shape, (16, 5, 5, 64))
  
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 six.moves import range
  from six.moves import zip
  from tensor2tensor.models.research import glow
  from tensor2tensor.models.research import glow_ops
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import hparam
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 6 for calling the GlowOpsTest obj's underlying member method code with example usage: obj.test_conv2d() without return types