code: def data_with_only_categorical_features():
      """Generate tabular data with only categorical features. All of them are binary."""
      supp_data = SupplementaryData(
          col_type_ids={"features": np.array([TYPE_TO_ID[str]] * 3)}
      )
      task = Task(TaskTypesEnum.regression)
      features = np.array(
          [["'a'", "0", "1"], ["'b'", "1", "0"], ["'c'", "1", "0"]],
          dtype=object,
      )
      input_data = InputData(
          idx=np.array([0, 1, 2]),
          features=features,
          target=np.array([0, 1, 2]),
          task=task,
          data_type=DataTypesEnum.table,
          supplementary_data=supp_data,
      )
  
      return input_data
  
imports: import numpy
  from fedot import Fedot
  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.preprocessing.data_types import TYPE_TO_ID
label_desc: Use image node_id 1 for calling a global function with example usage: data_with_only_categorical_features() and returns: input_data