code: def data_with_binary_features_and_categorical_target():
      """
      A dataset is generated where features and target require transformations.
      The categorical binary features and categorical target must be converted to int
      """
      task = Task(TaskTypesEnum.classification)
      features = np.array(
          [
              ["red", "blue"],
              ["red", "blue"],
              ["red", "blue"],
              [np.nan, "blue"],
              ["green", "blue"],
              ["green", "orange"],
              ["red", "orange"],
          ]
      )
      target = np.array(
          [
              "red-blue",
              "red-blue",
              "red-blue",
              "red-blue",
              "green-blue",
              "green-orange",
              "red-orange",
          ]
      )
      train_input = InputData(
          idx=[0, 1, 2, 3, 4, 5, 6],
          features=features,
          target=target,
          task=task,
          data_type=DataTypesEnum.table,
          supplementary_data=SupplementaryData(),
      )
  
      return train_input
  
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 8 for calling a global function with example usage: data_with_binary_features_and_categorical_target() and returns: train_input