class_docstr: Plotting object allows for visualization of absolute group bias metrics and
  relative disparities calculated by Aequitas Group(), Bias(), and Fairness()
  class instances.
docstr: Ensure multiple models in df, return model_ids if so
code: def _check_multiple_models(cls, df, method_table_name):
      """
      Ensure multiple models in df, return model_ids if so
      """
      if "model_id" in df.columns:
          df_models = df.model_id.unique()
          if len(df_models) < 2:
              raise ValueError(
                  "This method requires at least two distinct 'model_id' values "
                  f"in the dataframe. Tip: Check that "
                  f"{method_table_name}.model_id.unique() returns more than one element."
              )
          else:
              return df_models
      else:
          raise ValueError(
              "This method requires at least two distinct 'model_id' values "
              f"in the dataframe. Tip: Check that a 'model_id column exists in "
              f"'{method_table_name}'."
          )
  
imports: import logging
  import math
  import numpy
  import collections
  import seaborn
  import matplotlib.pyplot
  import matplotlib.colors
  import matplotlib.cm
  from aequitas import squarify_flipped
label_desc: Use image node_id 14 for calling the Plot obj's underlying member method code with example usage: obj._check_multiple_models(cls, df, method_table_name) and returns: df_models