code: def test_data_from_csv():
      test_file_path = str(os.path.dirname(__file__))
      file = "../../data/simple_classification.csv"
      task = Task(TaskTypesEnum.classification)
      df = pd.read_csv(os.path.join(test_file_path, file))
      data_array = np.array(df).T
      features = data_array[1:-1].T
      target = data_array[-1]
      idx = data_array[0]
      expected_features = InputData(
          features=features,
          target=target,
          idx=idx,
          task=task,
          data_type=DataTypesEnum.table,
      ).features
      actual_features_from_csv = InputData.from_csv(
          os.path.join(test_file_path, file)
      ).features
      assert np.array_equal(expected_features, actual_features_from_csv)
      df.set_index("ID", drop=True, inplace=True)
      features = df[df.columns[:-1]]
      target = df[df.columns[-1]]
      actual_features_from_df = InputData.from_dataframe(
          features, target
      ).features
      assert np.array_equal(expected_features, actual_features_from_df)
  
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 4 for calling a global function with example usage: test_data_from_csv() without return types