code: def food_choice():
      """
      Model a categorical response using the 'categorical' family to test 'interpret'
      plotting functions for a model whose predictions have multiple response
      dimensions (levels).
      """
      length = [
          1.3,
          1.32,
          1.32,
          1.4,
          1.42,
          1.42,
          1.47,
          1.47,
          1.5,
          1.52,
          1.63,
          1.65,
          1.65,
          1.65,
          1.65,
          1.68,
          1.7,
          1.73,
          1.78,
          1.78,
          1.8,
          1.85,
          1.93,
          1.93,
          1.98,
          2.03,
          2.03,
          2.31,
          2.36,
          2.46,
          3.25,
          3.28,
          3.33,
          3.56,
          3.58,
          3.66,
          3.68,
          3.71,
          3.89,
          1.24,
          1.3,
          1.45,
          1.45,
          1.55,
          1.6,
          1.6,
          1.65,
          1.78,
          1.78,
          1.8,
          1.88,
          2.16,
          2.26,
          2.31,
          2.36,
          2.39,
          2.41,
          2.44,
          2.56,
          2.67,
          2.72,
          2.79,
          2.84,
      ]
      choice = [
          "I",
          "F",
          "F",
          "F",
          "I",
          "F",
          "I",
          "F",
          "I",
          "I",
          "I",
          "O",
          "O",
          "I",
          "F",
          "F",
          "I",
          "O",
          "F",
          "O",
          "F",
          "F",
          "I",
          "F",
          "I",
          "F",
          "F",
          "F",
          "F",
          "F",
          "O",
          "O",
          "F",
          "F",
          "F",
          "F",
          "O",
          "F",
          "F",
          "I",
          "I",
          "I",
          "O",
          "I",
          "I",
          "I",
          "F",
          "I",
          "O",
          "I",
          "I",
          "F",
          "F",
          "F",
          "F",
          "F",
          "F",
          "F",
          "O",
          "F",
          "I",
          "F",
          "F",
      ]
      sex = ["Male"] * 32 + ["Female"] * 31
      data = pd.DataFrame(
          {"choice": choice, "length": length, "sex": sex}
      )
      data["choice"] = pd.Categorical(
          data["choice"].map(
              {"I": "Invertebrates", "F": "Fish", "O": "Other"}
          ),
          ["Other", "Invertebrates", "Fish"],
          ordered=True,
      )
  
      model = bmb.Model(
          "choice ~ length + sex", data, family="categorical"
      )
      idata = model.fit(tune=500, draws=500, random_seed=1234)
      return model, idata
  
imports: from os.path import dirname, join
  import numpy
  import pandas
  import matplotlib.pyplot
  import pytest
  import bambi
  from bambi.interpret import plot_comparisons, plot_predictions, plot_slopes
label_desc: Use image node_id 3 for calling a global function with example usage: food_choice() and returns: model,  idata