docstr: Args:
      d_model: dimension of the model
      d_ff: dimension of the feedforward layer
      dropout: dropout probability
docstr_tok: ["Args",":","d_model",":","dimension","of","the","model","d_ff",":","dimension","of","the","feedforward","layer","dropout",":","dropout","probability"]
code: def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1):
      """
      Args:
          d_model: dimension of the model
          d_ff: dimension of the feedforward layer
          dropout: dropout probability
      """
      super().__init__()
      self.ffn = PositionWiseFeedForward(
          d_model, d_ff, dropout, nn.SiLU(), True, False, False, False
      )
  
imports: import random
  from typing import Callable
  import torch
  import torch.nn
  from pytorch_tabular.models.common.layers.activations import RSoftmax, entmax15
label_desc: Use image node_id 1 to create a new SwiGLU object from inherited base classes: nn with example: obj = SwiGLU(d_model, d_ff, dropout)