class_docstr: Default network for the glow bijector.
  
  This builds a 3 layer convolutional network, with relu activation functions
  and he_normal initializer. The first and third layers have default kernel
  shape of 3, and the second layer is a 1x1 convolution. This is the setup
  in the public version of Glow.
  
  The output of the convolutional network defines the components of an Affine
  transformation (i.e. y = m * x + b), where m, x, and b are all tensors of
  the same shape, and * indicates elementwise multiplication.
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docstr: Default network for glow bijector.
code: def __init__(self, input_shape, num_hidden=400, kernel_shape=3):
      """Default network for glow bijector."""
      # Default is scale and shift, so 2c outputs.
      this_nchan = input_shape[-1] * 2
      conv_last = functools.partial(
          tfkl.Conv2D,
          padding="same",
          kernel_initializer=tf_keras.initializers.zeros(),
          bias_initializer=tf_keras.initializers.zeros(),
      )
      super(GlowDefaultNetwork, self).__init__(
          [
              tfkl.Input(shape=input_shape),
              tfkl.Conv2D(
                  num_hidden,
                  kernel_shape,
                  padding="same",
                  kernel_initializer=tf_keras.initializers.he_normal(),
                  activation="relu",
              ),
              tfkl.Conv2D(
                  num_hidden,
                  1,
                  padding="same",
                  kernel_initializer=tf_keras.initializers.he_normal(),
                  activation="relu",
              ),
              conv_last(this_nchan, kernel_shape),
          ]
      )
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  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 exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import real_nvp
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_lu
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import transpose
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.util.deferred_tensor import TransformedVariable
  from tensorflow_probability.python.util.seed_stream import SeedStream
label_desc: Use image node_id 1 to create a new GlowDefaultNetwork object from inherited base classes: tfk with example: obj = GlowDefaultNetwork(input_shape, num_hidden, kernel_shape)