code: def test_target_data_from_csv_correct():
      """Function tests two ways of processing target columns in "from_csv"
      method
      """
      test_file_path = str(os.path.dirname(__file__))
      file = "../../data/multi_target_sample.csv"
      path = os.path.join(test_file_path, file)
      task = Task(TaskTypesEnum.regression)
  
      # Process one column
      target_column = "1_day"
      one_column_data = InputData.from_csv(
          path,
          target_columns=target_column,
          index_col="date",
          task=task,
      )
  
      # Process multiple target columns
      target_columns = [
          "1_day",
          "2_day",
          "3_day",
          "4_day",
          "5_day",
          "6_day",
          "7_day",
      ]
      seven_columns_data = InputData.from_csv(
          path,
          target_columns=target_columns,
          index_col="date",
          task=task,
      )
  
      assert one_column_data.target.shape == (197, 1)
      assert seven_columns_data.target.shape == (197, 7)
  
imports: import os
  from copy import deepcopy, copy
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import load_iris
  from fedot.core.data.data import InputData, get_df_from_csv
  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 Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
  from test.unit.tasks.test_forecasting import get_ts_data_with_dt_idx
label_desc: Use image node_id 7 for calling a global function with example usage: test_target_data_from_csv_correct() without return types