code: def data_setup():
      predictors, response = load_iris(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(TaskTypesEnum.classification),
          data_type=DataTypesEnum.table,
      )
      return 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 1 for calling a global function with example usage: data_setup() and returns: data