code: def test_predict_shape_multi_target(
      data_setup: Tuple[InputData, InputData, str]
  ):
      train, test, _, _ = data_setup
      simple_pipeline = Pipeline(PipelineNode("linear"))
      simple_pipeline.fit(input_data=train)
  
      target_shape = test.target.shape
      # Get converted data
      _, results = QualityMetric()._simple_prediction(
          simple_pipeline, test
      )
      predict_shape = results.predict.shape
      assert target_shape == predict_shape
  
imports: import json
  import sys
  from itertools import product
  from typing import Callable, Dict, Tuple, Union
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import load_breast_cancer, load_diabetes, load_linnerud, load_wine
  from fedot.core.composer.metrics import QualityMetric, ROCAUC
  from fedot.core.data.data import InputData, OutputData
  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.metrics_repository import ClassificationMetricsEnum, ComplexityMetricsEnum, MetricsRepository, RegressionMetricsEnum, TimeSeriesForecastingMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root, set_random_seed
label_desc: Use image node_id 9 for calling a global function with example usage: test_predict_shape_multi_target(data_setup) without return types