code: def posterior(
      name,
      hparams,
      targets,
      targets_mask,
      decoder_self_attention_bias,
      **kwargs,
  ):
      """Compute mu and sigma for diagonal normal posterior q(z|x,y)."""
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          decoder_input = drop_2d(
              targets, hparams.mode, hparams.posterior_2d_dropout
          )
          decoder_input = common_attention.add_timing_signal_1d(
              decoder_input
          )
          decoder_input = tf.nn.dropout(
              decoder_input, rate=hparams.layer_prepostprocess_dropout
          )
          decoder_output = transformer_decoder_layers(
              "block",
              n_layers=hparams.n_posterior_layers,
              decoder_input=decoder_input,
              hparams=hparams,
              decoder_self_attention_bias=decoder_self_attention_bias,
              **kwargs,
          )
          decoder_output = gops.dense_weightnorm(
              "h2o_out",
              decoder_output,
              hparams.latent_size * 2,
              targets_mask,
              init_scale=0.0,
              init=False,
          )
          return decoder_output
  
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_layers
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models.transformer import transformer_decoder_layer
  from tensor2tensor.models.transformer import transformer_encoder
  from tensor2tensor.models.transformer import transformer_prepare_encoder
  from tensor2tensor.utils import learning_rate
  from tensor2tensor.utils import mlperf_log
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 4 for calling a global function with example usage: posterior(name, hparams, targets, targets_mask, decoder__attention_bias) and returns: decoder_output