code: def get_drift_method(result_dict: Dict):
      """Return which drift scoring methods were in use.
  
      Parameters
      ----------
      result_dict : Dict
          the result dict of the drift check.
      Returns
      -------
      Tuple(str, str)
          the categorical scoring method and then the numeric scoring method.
  
      """
      result_df = pd.DataFrame(result_dict).T
      cat_mthod_arr = result_df[
          result_df["Method"].isin(SUPPORTED_CATEGORICAL_METHODS)
      ]["Method"]
      cat_method = cat_mthod_arr.iloc[0] if len(cat_mthod_arr) else None
  
      num_mthod_arr = result_df[
          result_df["Method"].isin(SUPPORTED_NUMERIC_METHODS)
      ]["Method"]
      num_method = num_mthod_arr.iloc[0] if len(num_mthod_arr) else None
  
      return cat_method, num_method
  
imports: from numbers import Number
  from typing import Dict, Optional, Tuple, Union
  import numpy
  import pandas
  from plotly.graph_objs import Figure
  from plotly.subplots import make_subplots
  from scipy.stats import chi2_contingency, wasserstein_distance
  from deepchecks.core import ConditionCategory, ConditionResult
  from deepchecks.core.errors import DeepchecksValueError, NotEnoughSamplesError
  from deepchecks.utils.dict_funcs import get_dict_entry_by_value
  from deepchecks.utils.distribution.plot import CategoriesSortingKind, drift_score_bar_traces, feature_distribution_traces
  from deepchecks.utils.distribution.preprocessing import preprocess_2_cat_cols_to_same_bins
  from deepchecks.utils.plot import DEFAULT_DATASET_NAMES
  from deepchecks.utils.strings import format_number
label_desc: Use image node_id 2 for calling a global function with example usage: get_drift_method(result_dict) and returns: cat_method,  num_method