code: def __init__(
      self,
      dim,
      head_num=4,
      activation=tf.nn.relu,
      renorm=False,
      **kwargs,
  ):
      super(AttentionAggregator, self).__init__(**kwargs)
      dim //= head_num
      self.attentions = [
          SingleAttentionAggregator(dim, activation, renorm)
          for _ in range(head_num)
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_euler.python.utils import layers
label_desc: Use image node_id 1 to create a new AttentionAggregator object from inherited base classes: layers with example: obj = AttentionAggregator(dim, head_num, activation, renorm)