code: def __init__(self, model_name, config: RNNConfig):
      super(RNNSeq2Seq, self).__init__(config)
      self.config = config
      self.model_name = model_name
      self.input_size = config.input_size
      self.hidden_size = config.hidden_size
      self.vocab_size = config.vocab_size
      self.num_layers = config.num_layers
      self.bias = config.bias
      self.dropout = config.dropout
      self.encoder_bidirectional = config.encoder_bidirectional
      self.pad_token_id = config.pad_token_id
      self.eos_token_id = config.eos_token_id
      self.bos_token_id = config.bos_token_id
  
      self.word_embeddings_decoder = nn.Embedding(
          num_embeddings=self.vocab_size, embedding_dim=self.input_size
      )
      self.lm_head = nn.Linear(
          in_features=self.hidden_size, out_features=self.vocab_size
      )
  
      self.loss_fct = CrossEntropyLoss(
          ignore_index=self.pad_token_id, reduction="mean"
      )
      self.main_input_name = "input_ids"
      # loading encoder and decoder
      self.encoder = RNNEncoder(self.model_name, self.config)
      self.decoder = RNNDecoder(self.model_name, self.config)
      self.att_projection = nn.Linear(
          in_features=self.hidden_size
          + self.hidden_size * int(self.encoder_bidirectional),
          out_features=self.hidden_size,
          bias=self.bias,
      )
      self.output_projection = nn.Linear(
          in_features=self.hidden_size * 2
          + self.hidden_size * int(self.encoder_bidirectional),
          out_features=self.hidden_size,
          bias=self.bias,
      )
  
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 RNNSeq2Seq object from inherited base classes: RNNPretrainedModel with example: obj = RNNSeq2Seq(model_name, config)