code: def _create_glow(self, actnorm=False):
      glow_net = functools.partial(
          glow.GlowDefaultNetwork, num_hidden=32
      )
      glow_exit = glow.GlowDefaultExitNetwork
      glow_bijector = glow.Glow(
          output_shape=self.output_shape,
          num_glow_blocks=2,
          num_steps_per_block=1,
          coupling_bijector_fn=glow_net,
          exit_bijector_fn=glow_exit,
          grab_after_block=[0.5, 0.5],
          use_actnorm=actnorm,
          seed=test_util.SeedStream(seed=42, salt="glow"),
      )
      sigm = sigmoid.Sigmoid(low=self.minval, high=self.maxval)
      output = chain.Chain([sigm, glow_bijector])
      return output
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import blockwise
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import composition
  from tensorflow_probability.python.bijectors import glow
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math.gradient import batch_jacobian
label_desc: Use image node_id 2 for calling the GlowTest obj's underlying member method code with example usage: obj._create_glow(actnorm) and returns: output