code: def create_test_data(
      self,
      nb_categories=500,
      nb_int_categories=50,
      max_category_size=50,
      test_data_rel_size=0.33,
  ):
      random.seed(2)
      np_random = np.random.default_rng(seed=2)
      int_categories = np_random.integers(
          low=1, high=nb_int_categories, size=nb_int_categories
      )
      str_categories = [
          "".join(
              random.choices(
                  string.ascii_uppercase + string.digits,
                  k=random.randint(7, 8),
              )
          )
          for _ in range(nb_categories - nb_int_categories)
      ]
      categories = list(int_categories) + str_categories
      category_sizes = np_random.integers(
          low=1, high=max_category_size, size=nb_categories
      )
      category_indexes = np.array(range(nb_categories), dtype=int)
      sample_category_indexes = np.repeat(
          category_indexes, category_sizes
      )
      np_random.shuffle(sample_category_indexes)
      samples = [categories[i] for i in sample_category_indexes]
      data_size = len(samples)
      priming_data = samples
      test_data = []
      if test_data_rel_size > 0.0:
          test_data_size = round(data_size * test_data_rel_size) + 1
          test_data = priming_data[:test_data_size]
      return priming_data, test_data
  
file_path: lightwood/tests/unit_tests/encoder/categorical/test_autoencoder.py
imports: import unittest
  from lightwood.encoder.categorical import CategoricalAutoEncoder
  import string
  import random
  import logging
  import numpy
  import pandas
  from sklearn.metrics import accuracy_score
  import torch
  from lightwood.helpers.log import log
label_desc: Use image node_id 1 for calling the TestAutoencoder obj's underlying member method code with example usage: obj.create_test_data(nb_categories, nb_int_categories, max_category_size, test_data_rel_size) and returns: priming_data,  test_data