class_docstr: :class:`~transformers.EncoderDecoderConfig` is the configuration class to store the configuration of a
  :class:`~transformers.EncoderDecoderModel`. It is used to instantiate an Encoder Decoder model according to the
  specified arguments, defining the encoder and decoder configs.
  
  Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
  outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
  
  Args:
      kwargs (`optional`):
          Dictionary of keyword arguments. Notably:
  
              - **encoder** (:class:`~transformers.PretrainedConfig`, `optional`) -- An instance of a configuration
                object that defines the encoder config.
              - **decoder** (:class:`~transformers.PretrainedConfig`, `optional`) -- An instance of a configuration
                object that defines the decoder config.
  
  Examples::
  
      >>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
  
      >>> # Initializing a BERT bert-base-uncased style configuration
      >>> config_encoder = BertConfig()
      >>> config_decoder = BertConfig()
  
      >>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
  
      >>> # Initializing a Bert2Bert model from the bert-base-uncased style configurations
      >>> model = EncoderDecoderModel(config=config)
  
      >>> # Accessing the model configuration
      >>> config_encoder = model.config.encoder
      >>> config_decoder  = model.config.decoder
      >>> # set decoder config to causal lm
      >>> config_decoder.is_decoder = True
      >>> config_decoder.add_cross_attention = True
  
      >>> # Saving the model, including its configuration
      >>> model.save_pretrained('my-model')
  
      >>> # loading model and config from pretrained folder
      >>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained('my-model')
      >>> model = EncoderDecoderModel.from_pretrained('my-model', config=encoder_decoder_config)
docstr: Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig`.
  
  Returns:
      :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
docstr_tok: ["Serializes","this","instance","to","a","Python","dictionary",".","Override","the","default","`","to_dict","(",")","`","from","`","PretrainedConfig","`",".","Returns",":",":","obj",":","`","Dict","[","str",",","any","]","`",":","Dictionary","of","all","the","attributes","that","make","up","this","configuration","instance",","]
code: def to_dict(self):
      """
      Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig`.
  
      Returns:
          :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
      """
      output = copy.deepcopy(self.__dict__)
      output["encoder"] = self.encoder.to_dict()
      output["decoder"] = self.decoder.to_dict()
      output["model_type"] = self.__class__.model_type
      return output
  
imports: import copy
  from ...configuration_utils import PretrainedConfig
  from fastNLP.core.log import logger
label_desc: Use image node_id 3 for calling the EncoderDecoderConfig obj's underlying member method code with example usage: obj.to_dict() and returns: output