code: def forward(self, src, src_mask=None, pos_embed=None, **kwargs):
      residual = src
      if self.normalize_before:
          src = self.norm1(src)
      q = k = self.with_pos_embed(src, pos_embed)
      src = self.self_attn(
          q, k, value=src, attn_mask=src_mask, **kwargs
      )
  
      src = residual + self.dropout1(src)
      if not self.normalize_before:
          src = self.norm1(src)
  
      residual = src
      if self.normalize_before:
          src = self.norm2(src)
      src = self.linear2(
          self.dropout(self.activation(self.linear1(src)))
      )
      src = residual + self.dropout2(src)
      if not self.normalize_before:
          src = self.norm2(src)
      return src
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from ppdet.core.workspace import register
  from ..layers import MultiHeadAttention, _convert_attention_mask
  from .utils import _get_clones
  from ..initializer import linear_init_, normal_, constant_, xavier_uniform_
label_desc: Use image node_id 4 for calling the TransformerEncoderLayer obj's underlying member method code with example usage: obj.forward(src, src_mask, pos_embed) and returns: src