code: def data_setup() -> InputData:
      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 numpy
  import pytest
  from golem.core.dag.linked_graph_node import LinkedGraphNode
  from sklearn.datasets import load_iris
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score, classification_report
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.operations.model import Model
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import DEFAULT_PARAMS_STUB, NESTED_PARAMS_LABEL
label_desc: Use image node_id 1 for calling a global function with example usage: data_setup() and returns: data