code: def test_forecast_with_not_ts_problem():
      model = Fedot(
          problem="classification", **TESTS_MAIN_API_DEFAULT_PARAMS
      )
      train_data, test_data, _ = get_dataset("classification")
      model.fit(train_data, predefined_model="auto")
      with pytest.raises(ValueError):
          model.forecast(pre_history=test_data)
  
imports: import os
  import shutil
  from copy import deepcopy
  import numpy
  import pandas
  import pytest
  from golem.core.dag.graph_utils import graph_structure
  from sklearn.datasets import load_iris
  from sklearn.preprocessing import LabelEncoder
  from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline
  from fedot import Fedot
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.tasks import TsForecastingParams
  from test.data.datasets import get_dataset, get_multimodal_ts_data, load_categorical_unimodal, load_categorical_multidata
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.tasks.test_multi_ts_forecast import get_multi_ts_data
label_desc: Use image node_id 18 for calling a global function with example usage: test_forecast_with_not_ts_problem() without return types