code: def __init__(
      self,
      input_size,
      hidden_size,
      output_size=None,
      context_hidden_size=None,
      dropout=0,
  ):
      super().__init__()
  
      self.layer_norm = MaybeLayerNorm(
          output_size, hidden_size, eps=1e-3
      )
      self.lin_a = nn.Linear(input_size, hidden_size)
      if context_hidden_size is not None:
          self.lin_c = nn.Linear(
              context_hidden_size, hidden_size, bias=False
          )
      self.lin_i = nn.Linear(hidden_size, hidden_size)
      self.glu = GLU(
          hidden_size, output_size if output_size else hidden_size
      )
      self.dropout = nn.Dropout(dropout)
      self.out_proj = (
          nn.Linear(input_size, output_size) if output_size else None
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import LayerNorm
  import logging
  import warnings
  from typing import Tuple, Optional
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 1 to create a new GRN object from inherited base classes: nn with example: obj = GRN(input_size, hidden_size, output_size, context_hidden_size, dropout)