class_docstr: Positionwise feed forward layer.
  
  Args:
      input_dim (int): input dimension.
      hidden_dim (int): hidden dimension.
      dropout (float, optional): dropout probability. (Default: 0.0)
class_docstr_tok: ["Positionwise","feed","forward","layer",".","Args",":","input_dim","(","int",")",":","input","dimension",".","hidden_dim","(","int",")",":","hidden","dimension",".","dropout","(","float",",","optional",")",":","dropout","probability",".","(","Default",":","0.0",")"]
code: def __init__(
      self, input_dim: int, hidden_dim: int, dropout: float = 0.0
  ) -> None:
      super().__init__()
      self.sequential = torch.nn.Sequential(
          torch.nn.LayerNorm(input_dim),
          torch.nn.Linear(input_dim, hidden_dim, bias=True),
          torch.nn.SiLU(),
          torch.nn.Dropout(dropout),
          torch.nn.Linear(hidden_dim, input_dim, bias=True),
          torch.nn.Dropout(dropout),
      )
  
imports: from typing import Optional, Tuple
  import torch
label_desc: Use image node_id 1 to create a new _FeedForwardModule object from inherited base classes: torch.nn with example: obj = _FeedForwardModule(input_dim, hidden_dim, dropout)