code: def data_with_mixed_types_in_each_column(multi_output: bool = False):
      """
      Generate dataset with columns, which contain several data types (int, float or str).
      Moreover, columns can contain nans and target columns have the same problems also.
  
      :param multi_output: is there a need to generate multi-output target
      """
      task = Task(TaskTypesEnum.classification)
      features = np.array(
          [
              [np.nan, "1", np.nan, "6", "b"],
              [np.nan, "2", 1, "5", "a"],
              [np.nan, np.nan, 2.1, 4.1, 4],
              [np.nan, "3", 3, 3.5, 3],
              [np.nan, 8, 4, 2, 2],
              [np.nan, 8, "a", 1, 1],
              [np.nan, np.nan, np.nan, np.nan, np.nan],
              [np.nan, "4", "b", 0, 0],
              [np.nan, "5", 1, -1, -1],
          ],
          dtype=object,
      )
      if multi_output:
          # Multi-label classification problem solved
          target = np.array(
              [
                  ["label_1", 2],
                  ["label_1", 3],
                  ["label_0", 4],
                  ["label_0", 5],
                  ["label_0", 5],
                  [1, "6"],
                  [0, "7"],
                  [1, "8"],
                  [0, "9"],
              ],
              dtype=object,
          )
      else:
          target = np.array(
              [
                  "label_1",
                  "label_1",
                  "label_0",
                  "label_0",
                  "label_0",
                  1,
                  0,
                  1,
                  0,
              ],
              dtype=object,
          )
      input_data = InputData(
          idx=np.arange(9),
          features=features,
          target=target,
          task=task,
          data_type=DataTypesEnum.table,
          supplementary_data=SupplementaryData(),
      )
      return input_data
  
imports: import numpy
  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.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import TaskTypesEnum, Task
  from fedot.preprocessing.categorical import BinaryCategoricalPreprocessor
  from fedot.preprocessing.data_types import TableTypesCorrector
  from fedot.preprocessing.structure import DEFAULT_SOURCE_NAME
  from test.unit.data_operations.test_data_operations_implementations import get_mixed_data
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_only_categorical_features, data_with_too_much_nans, data_with_spaces_and_nans_in_features, data_with_nans_in_target_column, data_with_nans_in_multi_target, data_with_categorical_target
label_desc: Use image node_id 1 for calling a global function with example usage: data_with_mixed_types_in_each_column(multi_output) and returns: input_data