code: def __init__(self, model_name, config):
      super(RNNDecoder, self).__init__(config)
      self.model_name = model_name
      self.input_size = config.input_size
      self.hidden_size = config.hidden_size
      self.num_layers = config.num_layers
      self.dropout = config.dropout
      self.Dropout_layer = nn.Dropout(self.dropout)
      self.bias = config.bias
      if self.model_name.lower() == "gru":
          self.model_class = nn.GRUCell
      elif self.model_name.lower() == "lstm":
          self.model_class = nn.LSTMCell
      else:
          self.model_class = nn.RNNCell
      self.cells = []
      self.cells.append(
          self.model_class(
              input_size=self.input_size
              + config.encoder_bidirectional * self.hidden_size
              + self.hidden_size,
              hidden_size=self.hidden_size,
              bias=self.bias,
          )
      )
      for _ in range(self.num_layers - 1):
          self.cells.append(
              self.model_class(
                  input_size=self.hidden_size,
                  hidden_size=self.hidden_size,
                  bias=self.bias,
              )
          )
      self.cells = nn.ModuleList(self.cells)
  
imports: import inspect
  import warnings
  from typing import Optional, Tuple, Dict, Any
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.utils import ModelOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.configuration_utils import PretrainedConfig
  from torch.nn import CrossEntropyLoss
  from .abstract_model import AbstractModel
label_desc: Use image node_id 1 to create a new RNNDecoder object from inherited base classes: RNNPretrainedModel with example: obj = RNNDecoder(model_name, config)