class_docstr: attention layer 
class_docstr_tok: ["attention","layer"]
docstr: layer forward function 
docstr_tok: ["layer","forward","function"]
code: def forward(
      self, queries, keys, values, attn_bias, past_cache, key_tag=None
  ):
      """layer forward function"""
      assert (
          len(queries.shape)
          == len(keys.shape)
          == len(values.shape)
          == 3
      )
      # bsz, q_len, q_dim = queries.shape
      # bsz, k_len, k_dim = keys.shape
      # bsz, v_len, v_dim = values.shape
      # assert k_len == v_len
  
      q = self.q(queries)
      k = self.k(keys)
      v = self.v(values)
  
      cache = (k, v)
      if past_cache is not None:
          cached_k, cached_v = past_cache
          k = paddle.concat([cached_k, k], 1)
          v = paddle.concat([cached_v, v], 1)
  
      # [batch, head, seq, dim]
      q = q.reshape(
          [0, 0, self.n_head, q.shape[-1] // self.n_head]
      ).transpose([0, 2, 1, 3])
      # [batch, head, seq, dim]
      k = k.reshape(
          [0, 0, self.n_head, k.shape[-1] // self.n_head]
      ).transpose([0, 2, 1, 3])
      # [batch, head, seq, dim]
      v = v.reshape(
          [0, 0, self.n_head, v.shape[-1] // self.n_head]
      ).transpose([0, 2, 1, 3])
      q = q.scale(self.d_key**-0.5)
  
      score = q.matmul(k, transpose_y=True)
  
      if attn_bias is not None:
          score += attn_bias
      score = F.softmax(score)
      score = self.dropout(score)
      out = score.matmul(v)
  
      out = out.transpose([0, 2, 1, 3])
      out = out.reshape([0, 0, out.shape[2] * out.shape[3]])
      out = self.o(out)
  
      return out, cache
  
imports: import logging
  import re
  import time
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 2 for calling the AttentionLayer obj's underlying member method code with example usage: obj.forward(queries, keys, values, attn_bias, past_cache, key_tag) and returns: out,  cache