class_docstr: [`EncoderDecoderConfig`] is the configuration class to store the configuration of a [`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 [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      kwargs (*optional*):
          Dictionary of keyword arguments. Notably:
  
              - **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                the encoder config.
              - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                the decoder config.
  
  Examples:
  
  ```python
  >>> 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: Instantiate a [`EncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and
  decoder model configuration.
  
  Returns:
      [`EncoderDecoderConfig`]: An instance of a configuration object
docstr_tok: ["Instantiate","a","[","`","EncoderDecoderConfig","`","]","(","or","a","derived","class",")","from","a","pre-trained","encoder","model","configuration","and","decoder","model","configuration",".","Returns",":","[","`","EncoderDecoderConfig","`","]",":","An","instance","of","a","configuration","object"]
code: def from_encoder_decoder_configs(
      cls,
      encoder_config: PretrainedConfig,
      decoder_config: PretrainedConfig,
      **kwargs,
  ) -> PretrainedConfig:
      r"""
      Instantiate a [`EncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and
      decoder model configuration.
  
      Returns:
          [`EncoderDecoderConfig`]: An instance of a configuration object
      """
      logger.info(
          "Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"
      )
      decoder_config.is_decoder = True
      decoder_config.add_cross_attention = True
  
      return cls(
          encoder=encoder_config.to_dict(),
          decoder=decoder_config.to_dict(),
          **kwargs,
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/encoder_decoder/configuration_encoder_decoder.py
imports: import copy
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the EncoderDecoderConfig obj's underlying member method code with example usage: obj.from_encoder_decoder_configs(cls, encoder_config, decoder_config) and returns: cls