code: def iaf_flow(
      one_hot_assignments,
      scale_weights,
      scale_bias,
      num_codes,
      summary=True,
      name=None,
  ):
      """Performs a single IAF flow using scale and normalization transformations.
  
      Args:
        one_hot_assignments: Assignments Tensor with shape [num_samples, batch_size,
          latent_size, num_codes].
        scale_weights: Tensor corresponding to lower triangular matrix used to
          autoregressively generate scale matrix from assignments. To ensure the
          lower-triangular matrix has length of latent_size, scale_weights should
          be a rank-one tensor with size latent_size * (latent_size + 1) / 2.
        scale_bias: Bias tensor to be added to scale tensor, with shape
          [latent_size, num_codes]. If scale weights are zero, initialize scale_bias
          to be log(exp(1.) / 2. - 1) so initial transformation is identity.
        num_codes: Number of codes in codebook.
        summary: Whether to save summaries.
        name: String used for name scope.
  
      Returns:
        flow_output: Transformed one-hot assignments.
        inverse_log_det_jacobian: Inverse log deteriminant of Jacobian corresponding
          to transformation.
      """
      with tf.name_scope(name, default_name="iaf"):
          # Pad the one_hot_assignments by zeroing out the first latent dimension and
          # shifting the rest down by one (and removing the last dimension).
          padded_assignments = tf.pad(
              one_hot_assignments, [[0, 0], [0, 0], [1, 0], [0, 0]]
          )[:, :, :-1, :]
          scale_bijector = tfp.distributions.bijectors.Affine(
              scale_tril=tfp.math.fill_triangular(scale_weights)
          )
          scale = scale_bijector.forward(
              tf.transpose(padded_assignments, [0, 1, 3, 2])
          )
          # Transpose the bijector output since it performs a batch matmul.
          scale = tf.transpose(scale, [0, 1, 3, 2])
          scale = tf.nn.softplus(scale)
          scale = scale + tf.nn.softplus(
              scale_bias[tf.newaxis, tf.newaxis, ...]
          )
          # Don't need last dimension since the transformation keeps it constant.
          scale = scale[..., :-1]
  
          z = one_hot_assignments[..., :-1]
          unnormalized_probs = tf.concat(
              [z * scale, one_hot_assignments[..., -1, tf.newaxis]],
              axis=-1,
          )
          normalizer = tf.reduce_sum(unnormalized_probs, axis=-1)
          flow_output = unnormalized_probs / (
              normalizer[..., tf.newaxis]
          )
          inverse_log_det_jacobian = -tf.reduce_sum(
              tf.log(scale), axis=-1
          ) + num_codes * tf.log(normalizer)
          if summary:
              tf.summary.histogram("iaf/scale", tf.reshape(scale, [-1]))
              tf.summary.histogram(
                  "iaf/inverse_log_det_jacobian",
                  tf.reshape(inverse_log_det_jacobian, [-1]),
              )
          return flow_output, inverse_log_det_jacobian
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_image_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import transformer_layers
  from tensor2tensor.utils import beam_search
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
label_desc: Use image node_id 19 for calling a global function with example usage: iaf_flow(one_hot_assignments, scale_weights, scale_bias, num_codes, summary, name) and returns: flow_output,  inverse_log_det_jacobian