code: def data_with_text_features():
      """Generate tabular data with text features"""
      task = Task(TaskTypesEnum.classification)
      features = np.array(
          [
              "My mistress eyes are nothing like the sun.",
              "Coral is far more red than her lips red.",
              "If snow be white, why then her breasts are dun?",
              "If hairs be wires, black wires grow on her head.",
          ],
          dtype=object,
      )
  
      target = np.array([[0], [1], [0], [1]])
      train_input = InputData(
          idx=np.array([0, 1, 2, 3]),
          features=features,
          target=target,
          task=task,
          data_type=DataTypesEnum.text,
          supplementary_data=SupplementaryData(),
      )
  
      return train_input
  
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 7 for calling a global function with example usage: data_with_text_features() and returns: train_input