code: def __init__(
      self,
      input_size: int,
      hidden_size: int = None,
      skip_size: int = None,
      trainable_add: bool = False,
      dropout: float = None,
  ):
      super().__init__()
  
      self.input_size = input_size
      self.hidden_size = hidden_size or input_size
      self.skip_size = skip_size or self.hidden_size
      self.dropout = dropout
  
      self.glu = GatedLinearUnit(
          self.input_size,
          hidden_size=self.hidden_size,
          dropout=self.dropout,
      )
      self.add_norm = AddNorm(
          self.hidden_size,
          skip_size=self.skip_size,
          trainable_add=trainable_add,
      )
  
imports: import math
  from typing import Dict, List, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new GateAddNorm object from inherited base classes: nn with example: obj = GateAddNorm(input_size, hidden_size, skip_size, trainable_add, dropout)