code: def calculate_depend_matrix(
      data, depend_method, corr_method, agg_method
  ):
      depend_matrix = pd.DataFrame()
      if depend_method == "assoc":
          depend_matrix = calculate_assoc_matrix(data, corr_method)
      if depend_method == "pps":
          depend_matrix = calculate_pps_matrix(data, agg_method)
      if callable(depend_method):
          try:
              depend_matrix = depend_method(data)
          except:
              raise ValueError(
                  "You have passed wrong callable in depend_method argument. "
                  "'depend_method' is the callable to use for calculating dependency matrix."
              )
  
      # if there is a non-varying column in data, there will be NaN values in the 'depend_matrix'.
      # replace NaN values on the diagonal with 1 and others with 0.
      if depend_matrix.isnull().any(axis=None):
          warnings.warn(
              "There were NaNs in `depend_matrix`. This is possibly because there is a feature in the data with only one unique value. Replacing NaN values on the diagonal with 1 and others with 0."
          )
          depend_matrix[depend_matrix.isnull()] = 0
          for i in range(depend_matrix.shape[0]):
              depend_matrix.iloc[i, i] = 1
  
      return depend_matrix
  
file_path: DALEX/python/dalex/dalex/aspect/utils.py
imports: import numpy
  import pandas
  import warnings
  from .None import checks
  from ..None import _global_checks
label_desc: Use image node_id 5 for calling a global function with example usage: calculate_depend_matrix(data, depend_method, corr_method, agg_method) and returns: depend_matrix