class_docstr: This is the BaseConfig class which defines all the useful loading and saving methods
  of the configs
class_docstr_tok: ["This","is","the","BaseConfig","class","which","defines","all","the","useful","loading","and","saving","methods","of","the","configs"]
docstr: Creates a :class:`~pythae.config.BaseConfig` instance from a dictionnary
  
  Args:
      config_dict (dict): The Python dictionnary containing all the parameters
  
  Returns:
      :class:`BaseConfig`: The created instance
docstr_tok: ["Creates","a",":","class",":","`","~pythae.config.BaseConfig","`","instance","from","a","dictionnary","Args",":","config_dict","(","dict",")",":","The","Python","dictionnary","containing","all","the","parameters","Returns",":",":","class",":","`","BaseConfig","`",":","The","created","instance"]
code: def from_dict(cls, config_dict: Dict[str, Any]) -> "BaseConfig":
      """Creates a :class:`~pythae.config.BaseConfig` instance from a dictionnary
  
      Args:
          config_dict (dict): The Python dictionnary containing all the parameters
  
      Returns:
          :class:`BaseConfig`: The created instance
      """
      try:
          config = cls(**config_dict)
      except (ValidationError, TypeError) as e:
          raise e
      return config
  
file_path: benchmark_VAE/src/pythae/config.py
imports: import json
  import os
  import warnings
  from dataclasses import asdict, field
  from typing import Any, Dict, Union
  from pydantic import ValidationError
  from pydantic.dataclasses import dataclass
label_desc: Use image node_id 2 for calling the BaseConfig obj's underlying member method code with example usage: obj.from_dict(cls, config_dict) and returns: config