class_docstr: ### Feed-Forward Network
class_docstr_tok: ["#","#","#","Feed-Forward","Network"]
docstr: :param d_model: is the input embedding size
  :param d_mult: is multiplicative factor for the hidden layer size
docstr_tok: [":","param","d_model",":","is","the","input","embedding","size",":","param","d_mult",":","is","multiplicative","factor","for","the","hidden","layer","size"]
code: def __init__(self, d_model: int, d_mult: int = 4):
      """
      :param d_model: is the input embedding size
      :param d_mult: is multiplicative factor for the hidden layer size
      """
      super().__init__()
      self.net = nn.Sequential(
          GeGLU(d_model, d_model * d_mult),
          nn.Dropout(0.0),
          nn.Linear(d_model * d_mult, d_model),
      )
  
imports: from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new FeedForward object from inherited base classes: nn with example: obj = FeedForward(d_model, d_mult)