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)
code: def __init__(
      self,
      d_model,
      n_cat_embeddings,
      nhead,
      dim_feedforward=2048,
      dropout=0.1,
      activation="relu",
  ):
      super().__init__()
  
      self.self_attn = MultiheadAttention(
          d_model, n_cat_embeddings, nhead, dropout=dropout
      )
  
      # Implementation of Feedforward model
      self.linear1 = Linear(d_model, dim_feedforward)
      self.dropout = Dropout(dropout)
      self.linear2 = Linear(dim_feedforward, d_model)
  
      self.norm1 = LayerNorm(d_model)
      self.norm2 = LayerNorm(d_model)
      self.dropout1 = Dropout(dropout)
      self.dropout2 = Dropout(dropout)
  
      self.activation = _get_activation_fn(activation)
  
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 1 to create a new TransformerEncoderLayerModified object from inherited base classes: Module with example: obj = TransformerEncoderLayerModified(d_model, n_cat_embeddings, nhead, dim_feedforward, dropout, activation)