code: def additive_coupling(
      name,
      x,
      x_mask,
      inverse,
      split_dim,
      identity_first,
      init,
      decoder_self_attention_bias=None,
      **kwargs,
  ):
      """Additive coupling transform layer."""
      hparams = kwargs["hparams"]
      batch_size, length, n_channels = common_layers.shape_list(x)
      assert hparams.scale_width > 0.0 and hparams.scale_width < 1.0
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          x_id, x_tr, _, n_transform, bias, mask = gops.split_coupling(
              x,
              x_mask,
              split_dim,
              identity_first,
              decoder_self_attention_bias,
          )
          z_id = x_id
  
          loc = gops.transformer_decoder_block(
              "theta_tr",
              n_layers=hparams.n_layers_transform_params,
              x=x_id,
              x_mask=mask,
              output_size=n_transform,
              init=init,
              decoder_self_attention_bias=bias,
              **kwargs,
          )
          if not inverse:
              z_tr = x_tr + loc
          else:
              z_tr = x_tr - loc
          logabsdet = tf.constant(0.0, dtype=tf.float32)
  
          tf.summary.histogram("_loc", tf.boolean_mask(loc, mask))
          result = gops.join_coupling(
              z_id, z_tr, split_dim, identity_first
          )
          result = tf.reshape(result, [batch_size, length, n_channels])
          return result, logabsdet
  
file_path: tensor2tensor/tensor2tensor/layers/transformer_glow_layers.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import scipy
  from tensor2tensor.layers import common_layers
  import tensor2tensor.layers.transformer_glow_layers_ops
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: additive_coupling(name, x, x_mask, inverse, split_dim, identity_first, init, decoder__attention_bias) and returns: result,  logabsdet