code: def __init__(
      self,
      input_size: int,
      hidden_size: int,
      output_size: int,
      dropout: float = 0.1,
      context_size: int = None,
      residual: bool = False,
  ):
      super().__init__()
      self.input_size = input_size
      self.output_size = output_size
      self.context_size = context_size
      self.hidden_size = hidden_size
      self.dropout = dropout
      self.residual = residual
  
      if self.input_size != self.output_size and not self.residual:
          residual_size = self.input_size
      else:
          residual_size = self.output_size
  
      if self.output_size != residual_size:
          self.resample_norm = ResampleNorm(
              residual_size, self.output_size
          )
  
      self.fc1 = nn.Linear(self.input_size, self.hidden_size)
      self.elu = nn.ELU()
  
      if self.context_size is not None:
          self.context = nn.Linear(
              self.context_size, self.hidden_size, bias=False
          )
  
      self.fc2 = nn.Linear(self.hidden_size, self.hidden_size)
      self.init_weights()
  
      self.gate_norm = GateAddNorm(
          input_size=self.hidden_size,
          skip_size=self.output_size,
          hidden_size=self.output_size,
          dropout=self.dropout,
          trainable_add=False,
      )
  
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 GatedResidualNetwork object from inherited base classes: nn with example: obj = GatedResidualNetwork(input_size, hidden_size, output_size, dropout, context_size, residual)