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 single model in df, return model_id if so
code: def _check_model_id(cls, df, method_table_name):
      """
      Ensure single model in df, return model_id if so
      """
      if "model_id" in df.columns:
          df_models = df.model_id.unique()
          if len(df_models) != 1:
              raise ValueError(
                  "This method requires one and only one model_id in the dataframe. "
                  f"Tip: Check that {method_table_name}.model_id.unique() returns a one-element array. "
              )
          else:
              return df_models[0]
      else:
          return 0
  
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 13 for calling the Plot obj's underlying member method code with example usage: obj._check_model_id(cls, df, method_table_name) and returns: int, df_models