code: def check_mask_params(mask_params):
      """
      Check if mask_params given respect the expected format.
  
      Parameters
      ----------
      mask_params: dict (optional)
          Dictionnary allowing the user to define a apply a filter to summarize the local explainability.
      """
      if not isinstance(mask_params, dict):
          raise ValueError(
              """
              mask_params must be a dict  
              """
          )
      else:
          conform_arguments = [
              "features_to_hide",
              "threshold",
              "positive",
              "max_contrib",
          ]
          mask_arguments_not_conform = [
              argument
              for argument in mask_params.keys()
              if argument not in conform_arguments
          ]
          if len(mask_arguments_not_conform) != 0:
              raise ValueError(
                  """
              mask_params must only have the following key arguments:
              -feature_to_hide
              -threshold
              -positive
              -max_contrib 
              """
              )
  
imports: import copy
  import numpy
  import pandas
  from shapash.utils.category_encoder_backend import no_dummies_category_encoder, supported_category_encoder, dummies_category_encoder
  from shapash.utils.columntransformer_backend import no_dummies_sklearn, supported_sklearn
  from shapash.utils.model import extract_features_model
  from shapash.utils.model_synoptic import dict_model_feature
  from shapash.utils.transform import preprocessing_tolist, check_transformers
  from shapash.utils.columntransformer_backend import columntransformer, get_feature_names, get_list_features_names
label_desc: Use image node_id 4 for calling a global function with example usage: check_mask_params(mask_params) without return types