code: def load_categorical_multidata():
      # Create features table
      features_first = np.array(
          [
              [0, "  a"],
              [1, " a "],
              [2, "  b"],
              [3, np.nan],
              [4, "  a"],
              [5, "  b"],
              [6, "b  "],
              [7, "  c"],
              [8, " c "],
          ],
          dtype=object,
      )
      features_second = np.array(
          [
              [10, "  a"],
              [11, " a "],
              [12, "  b"],
              [13, " a "],
              [14, "  a"],
              [15, "  b"],
              [16, "b  "],
              [17, "  c"],
              [18, " c "],
          ],
          dtype=object,
      )
      # TODO @andreygetmanov (fails if target = ['true', 'false', ...])
      target = np.array([1, 0, 1, 0, 0, 0, 0, 1, 1])
  
      fit_data = {"first": features_first, "second": features_second}
  
      return fit_data, target
  
imports: import os
  from typing import Optional
  import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from cases.metocean_forecasting_problem import prepare_input_data
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
  from test.integration.models.test_split_train_test import get_synthetic_input_data
  from test.unit.tasks.test_classification import get_iris_data, get_synthetic_classification_data
  from test.unit.tasks.test_forecasting import get_ts_data
  from test.unit.tasks.test_regression import get_synthetic_regression_data
label_desc: Use image node_id 7 for calling a global function with example usage: load_categorical_multidata() and returns: fit_data,  target