code: def data_for_task_type(request) -> Tuple[InputData, Pipeline]:
      task_type = request.param
      if task_type == TaskTypesEnum.classification:
          load_func = load_iris
          pipeline = get_simple_pipeline("rf")
      elif task_type == TaskTypesEnum.regression:
          load_func = load_diabetes
          pipeline = get_simple_pipeline("rfr")
      else:
          raise ValueError(f"Unsupported task type: {task_type}")
      predictors, response = load_func(return_X_y=True)
      np.random.shuffle(predictors)
      np.random.shuffle(response)
      predictors = predictors[:100]
      response = response[:100]
      data = InputData(
          features=predictors,
          target=response,
          idx=np.arange(0, 100),
          task=Task(task_type),
          data_type=DataTypesEnum.table,
      )
      return data, pipeline
  
file_path: FEDOT/test/unit/explainability/test_pipeline_explanation.py
imports: from copy import deepcopy
  from typing import Tuple
  import numpy
  import pytest
  from sklearn.datasets import load_iris, load_diabetes
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  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.explainability.explainers import explain_pipeline
  from fedot.explainability.surrogate_explainer import SurrogateExplainer, get_simple_pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: data_for_task_type(request) and returns: data,  pipeline