class_docstr: TransformerEncoderLayer is made up of self-attn and feedforward network.
  This standard encoder layer is based on the paper "Attention Is All You Need".
  Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
  Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
  Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
  in a different way during application.
  
  Args:
      d_model: the number of expected features in the input (required).
      nhead: the number of heads in the multiheadattention models (required).
      dim_feedforward: the dimension of the feedforward network model (default=2048).
      dropout: the dropout value (default=0.1).
      activation: the activation function of intermediate layer, relu or gelu (default=relu).
  
  Examples::
      >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
      >>> src = torch.rand(10, 32, 512)
      >>> out = encoder_layer(src)
docstr: Pass the input through the encoder layer.
  
  Args:
      src: the sequence to the encoder layer (required).
      src_mask: the mask for the src sequence (optional).
      src_key_padding_mask: the mask for the src keys per batch (optional).
  
  Shape:
      see the docs in Transformer class.
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code: def forward(
      self,
      src: torch.Tensor,
      src_mask: Optional[torch.Tensor] = None,
      src_key_padding_mask: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      """Pass the input through the encoder layer.
  
      Args:
          src: the sequence to the encoder layer (required).
          src_mask: the mask for the src sequence (optional).
          src_key_padding_mask: the mask for the src keys per batch (optional).
  
      Shape:
          see the docs in Transformer class.
      """
      src2 = self.self_attn(
          src,
          src,
          src,
          attn_mask=src_mask,
          key_padding_mask=src_key_padding_mask,
      )[0]
      src = src + self.dropout1(src2)
      src = self.norm1(src)
      src2 = self.linear2(
          self.dropout(self.activation(self.linear1(src)))
      )
      src = src + self.dropout2(src2)
      src = self.norm2(src)
      return src
  
imports: import math
  from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  from torch.nn import Module, init
  from torch.nn.functional import dropout, linear, softmax
  from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
  from torch.nn.modules.dropout import Dropout
  from torch.nn.modules.normalization import LayerNorm
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 3 for calling the TransformerEncoderLayerModified obj's underlying member method code with example usage: obj.forward(src, src_mask, src_key_padding_mask) and returns: src