class_docstr: Inspired by torch.nn.TransformerEncoderLayer and
  rwightman's timm package.
class_docstr_tok: ["Inspired","by","torch.nn.TransformerEncoderLayer","and","rwightman","'s","timm","package","."]
code: def __init__(
      self,
      d_model,
      nhead,
      dim_feedforward=2048,
      dropout=0.1,
      attention_dropout=0.1,
      drop_path_rate=0.1,
  ):
      super().__init__()
  
      self.pre_norm = nn.LayerNorm(d_model)
      self.self_attn = Attention(
          dim=d_model,
          num_heads=nhead,
          attention_dropout=attention_dropout,
          projection_dropout=dropout,
      )
  
      self.linear1 = nn.Linear(d_model, dim_feedforward)
      self.dropout1 = nn.Dropout(dropout)
      self.norm1 = nn.LayerNorm(d_model)
      self.linear2 = nn.Linear(dim_feedforward, d_model)
      self.dropout2 = nn.Dropout(dropout)
  
      self.drop_path = DropPath(drop_path_rate)
  
      self.activation = F.gelu
  
imports: import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
label_desc: Use image node_id 1 to create a new TransformerEncoderLayer object from inherited base classes: nn with example: obj = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, attention_dropout, drop_path_rate)