code: def __init__(self, config, tokenizer):
      super().__init__(config, tokenizer)
  
      # initialize model
      self.configuration = RNNConfig(
          input_size=config["input_size"],
          hidden_size=config["hidden_size"],
          vocab_size=config["vocab_size"],
          num_layers=config["num_layers"],
          bias=config["bias"],
          dropout=config["dropout"],
          encoder_bidirectional=config["encoder_bidirectional"],
          pad_token_id=config["pad_token_id"],
          eos_token_id=config["eos_token_id"],
          bos_token_id=config["bos_token_id"],
      )
      self.model = RNNSeq2Seq(config["model_name"], self.configuration)
      self.generate_setting(config)
  
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 RNN_Models object from inherited base classes: AbstractModel with example: obj = RNN_Models(config, tokenizer)