code: def __init__(self, cfg, name=None):
      super(AttentionLayer, self).__init__()
      initializer = F.initializer.TruncatedNormal(
          scale=cfg["initializer_range"]
      )
      d_model = cfg["hidden_size"]
      n_head = cfg["num_attention_heads"]
      assert d_model % n_head == 0
      d_model_q = (
          cfg.get("query_hidden_size_per_head", d_model // n_head)
          * n_head
      )
      d_model_v = (
          cfg.get("value_hidden_size_per_head", d_model // n_head)
          * n_head
      )
      self.n_head = n_head
      self.d_key = d_model_q // n_head
      self.q = _build_linear(
          d_model, d_model_q, append_name(name, "query_fc"), initializer
      )
      self.k = _build_linear(
          d_model, d_model_q, append_name(name, "key_fc"), initializer
      )
      self.v = _build_linear(
          d_model, d_model_v, append_name(name, "value_fc"), initializer
      )
      self.o = _build_linear(
          d_model_v,
          d_model,
          append_name(name, "output_fc"),
          initializer,
      )
      self.dropout = (
          lambda i: L.dropout(
              i,
              dropout_prob=cfg["attention_probs_dropout_prob"],
              dropout_implementation="upscale_in_train",
          )
          if self.training
          else i
      )
  
imports: from __future__ import division
  from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import unicode_literals
  import logging
  import paddle.fluid.dygraph
  import paddle.fluid
  import paddle.fluid.layers
label_desc: Use image node_id 1 to create a new AttentionLayer object from inherited base class: D with example: obj = AttentionLayer(cfg, name)