code: def test_pipeline_with_wrong_data():
      pipeline = Pipeline(PipelineNode("linear"))
      data_seq = np.arange(0, 10)
      task = Task(
          TaskTypesEnum.ts_forecasting,
          TsForecastingParams(forecast_length=10),
      )
  
      data = InputData(
          idx=data_seq,
          features=data_seq,
          target=data_seq,
          data_type=DataTypesEnum.ts,
          task=task,
      )
  
      with pytest.raises(ValueError):
          pipeline.fit(data)
  
imports: import datetime
  import os
  import platform
  import time
  from copy import deepcopy
  from multiprocessing import set_start_method
  import numpy
  import pytest
  from sklearn.datasets import load_iris
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  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, TsForecastingParams
  from fedot.core.utils import probs_to_labels
  from fedot.preprocessing.preprocessing import DataPreprocessor
  from test.integration.composer.test_composer import to_categorical_codes
  from test.integration.models.test_model import classification_dataset_with_redundant_features
  from test.unit.dag.test_graph_operator import get_pipeline
  from test.unit.pipelines.test_pipeline_comparison import pipeline_first
  from test.unit.tasks.test_forecasting import get_ts_data
label_desc: Use image node_id 10 for calling a global function with example usage: test_pipeline_with_wrong_data() without return types