code: def load_categorical_unimodal():
      dataset_path = "test/data/classification_with_categorical.csv"
      full_path = os.path.join(str(fedot_project_root()), dataset_path)
      data = InputData.from_csv(full_path)
      train_data, test_data = train_test_data_setup(data, shuffle=True)
  
      return train_data, test_data
  
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 6 for calling a global function with example usage: load_categorical_unimodal() and returns: train_data,  test_data