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",")"]
docstr: Args:
      input (torch.Tensor): with shape `(*, D)`.
  
  Returns:
      torch.Tensor: output, with shape `(*, D)`.
docstr_tok: ["Args",":","input","(","torch.Tensor",")",":","with","shape","`","(","*",",","D",")","`",".","Returns",":","torch.Tensor",":","output",",","with","shape","`","(","*",",","D",")","`","."]
code: def forward(self, input: torch.Tensor) -> torch.Tensor:
      r"""
      Args:
          input (torch.Tensor): with shape `(*, D)`.
  
      Returns:
          torch.Tensor: output, with shape `(*, D)`.
      """
      return self.sequential(input)
  
imports: from typing import Optional, Tuple
  import torch
label_desc: Use image node_id 2 for calling the _FeedForwardModule obj's underlying member method code with example usage: obj.forward(input) and returns: self