code: def _get_categorical_support(n_features, fit_params):
      """Obtain boolean mask for categorical features"""
  
      cat_support = np.zeros(n_features, dtype=np.bool)
      cat_ids = []
  
      msg = (
          "When manually setting categarical features, "
          "pass a 1D array-like of categorical columns indices "
          "(specified as integers)."
      )
  
      if "categorical_feature" in fit_params:  # LGB
          cat_ids = fit_params["categorical_feature"]
          if len(np.shape(cat_ids)) != 1:
              raise ValueError(msg)
          if not all([isinstance(c, int) for c in cat_ids]):
              raise ValueError(msg)
  
      cat_support[cat_ids] = True
  
      return cat_support
  
file_path: shap-hypetune/shaphypetune/utils.py
imports: import random
  import numpy
  from itertools import product
  from shap import TreeExplainer
label_desc: Use image node_id 4 for calling a global function with example usage: _get_categorical_support(n_features, fit_params) and returns: cat_support