code: def identify_algorithm_hyperparameters(
      model_initializer,
  ):  # FLAG: Play nice with Keras
      """Determine keyword-arguments accepted by `model_initializer`, along with their default values
  
      Parameters
      ----------
      model_initializer: functools.partial, or class, or class instance
          The algorithm class being used to initialize a model
  
      Returns
      -------
      hyperparameter_defaults: dict
          The dict of kwargs accepted by `model_initializer` and their default values
      """
  
      hyperparameter_defaults = dict()
  
      # FLAG: Play nice with Keras
      try:
          signature_parameters = signature(model_initializer).parameters
      except TypeError:
          signature_parameters = signature(
              model_initializer.__class__
          ).parameters
  
      for k, v in signature_parameters.items():
          if (v.kind == v.KEYWORD_ONLY) or (
              v.kind == v.POSITIONAL_OR_KEYWORD
          ):
              hyperparameter_defaults[k] = v.default
  
      return hyperparameter_defaults
  
label_desc: Use image node_id 2 for calling a global function with example usage: identify_algorithm_hyperparameters(model_initializer) and returns: hyperparameter_defaults