code: def compress_self_attention_layer(x, hparams, name=None):
      """Attend function."""
      with tf.variable_scope(
          name, default_name="compress_self_attention"
      ):
          x, xshape, _ = cia.maybe_reshape_4d_to_3d(x)
          y = common_attention.multihead_attention(
              common_layers.layer_preprocess(x, hparams),
              None,
              None,
              hparams.attention_key_channels or hparams.hidden_size,
              hparams.attention_value_channels or hparams.hidden_size,
              hparams.hidden_size,
              hparams.num_heads,
              hparams.attention_dropout,
          )
          res = common_layers.layer_postprocess(x, y, hparams)
          return tf.reshape(res, xshape)
  
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 1 for calling a global function with example usage: compress_self_attention_layer(x, hparams, name) and returns: tf