code: def data_with_too_much_nans():
      """Generate tabular data with too much nan's in numpy array (inf values also must be signed as nan).
      Columns with ids 1 and 2 have nans more than 90% in their structure.
      """
      task = Task(TaskTypesEnum.regression)
      features = np.array(
          [
              [1, np.inf, np.nan],
              [np.nan, np.inf, np.nan],
              [3, np.inf, np.nan],
              [7, np.inf, np.nan],
              [8, np.nan, np.nan],
              [np.nan, np.nan, 23],
              [9, np.inf, np.nan],
              [9, np.inf, np.nan],
              [9, np.inf, np.nan],
              [9, "1", np.inf],
              [8, np.nan, np.inf],
          ],
          dtype=object,
      )
      target = np.array(
          [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]]
      )
      train_input = InputData(
          idx=np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
          features=features,
          target=target,
          task=task,
          data_type=DataTypesEnum.table,
          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 2 for calling a global function with example usage: data_with_too_much_nans() and returns: train_input