code: def __init__(
      self,
      input_size=768,
      hidden_size=768,
      vocab_size=50265,
      num_layers=1,
      bias=True,
      dropout=0.0,
      encoder_bidirectional=True,
      pad_token_id=1,
      eos_token_id=2,
      bos_token_id=0,
      num_labels=3,
      is_encoder_decoder=True,
      forced_eos_token_id=2,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.dropout = dropout
      self.num_layers = num_layers
      self.bias = bias
      self.encoder_bidirectional = encoder_bidirectional
      super().__init__(
          num_labels=num_labels,
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          is_encoder_decoder=is_encoder_decoder,
          forced_eos_token_id=forced_eos_token_id,
          **kwargs,
      )
  
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 RNNConfig object from inherited base classes: PretrainedConfig with example: obj = RNNConfig(input_size, hidden_size, vocab_size, num_layers, bias, dropout, encoder_bidirectional, pad_token_id, eos_token_id, bos_token_id, num_labels, is_encoder_decoder, forced_eos_token_id)