code: def data_setup():
      task = Task(TaskTypesEnum.classification)
      predictors, response = load_breast_cancer(return_X_y=True)
      np.random.shuffle(predictors)
      np.random.shuffle(response)
      response = response[:100]
      predictors = predictors[:100]
  
      input_data = InputData(
          idx=np.arange(0, len(predictors)),
          features=predictors,
          target=response,
          task=task,
          data_type=DataTypesEnum.table,
      )
      train_data, test_data = train_test_data_setup(data=input_data)
      train_data_x = train_data.features
      test_data_x = test_data.features
      train_data_y = train_data.target
      test_data_y = test_data.target
  
      train_data = InputData(
          features=train_data_x,
          target=train_data_y,
          idx=np.arange(0, len(train_data_y)),
          task=task,
          data_type=DataTypesEnum.table,
      )
      test_data = InputData(
          features=test_data_x,
          target=test_data_y,
          idx=np.arange(0, len(test_data_y)),
          task=task,
          data_type=DataTypesEnum.table,
      )
      return train_data, test_data
  
imports: import glob
  import os
  import numpy
  import pytest
  from sklearn.datasets import load_breast_cancer
  from fedot.core.caching.pipelines_cache import OperationsCache
  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
label_desc: Use image node_id 1 for calling a global function with example usage: data_setup() and returns: train_data,  test_data