code: def __init__(self, model_name, config):
      super(RNNEncoder, 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.bias = config.bias
      self.dropout = config.dropout
      self.bidirectional = config.encoder_bidirectional
      self.vocab_size = config.vocab_size
      self.word_embeddings_encoder = nn.Embedding(
          num_embeddings=self.vocab_size, embedding_dim=self.input_size
      )
      self.h_projection = nn.Linear(
          in_features=self.hidden_size
          + self.hidden_size * int(self.bidirectional),
          out_features=self.hidden_size,
          bias=self.bias,
      )
      self.c_projection = nn.Linear(
          in_features=self.hidden_size
          + self.hidden_size * int(self.bidirectional),
          out_features=self.hidden_size,
          bias=self.bias,
      )
      if self.model_name == "lstm":
          self.model_class = nn.LSTM
      elif self.model_name == "gru":
          self.model_class = nn.GRU
      else:
          self.model_class = nn.RNN
      self.encoder = self.model_class(
          input_size=self.input_size,
          hidden_size=self.hidden_size,
          num_layers=self.num_layers,
          bias=self.bias,
          batch_first=True,
          dropout=self.dropout,
          bidirectional=self.bidirectional,
      )
      self.main_input_name = "input_ids"
  
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 RNNEncoder object from inherited base classes: RNNPretrainedModel with example: obj = RNNEncoder(model_name, config)