code: def get_synthetic_input_data(
      n_samples=10000, n_features=10, random_state=None
  ) -> InputData:
      synthetic_data = make_classification(
          n_samples=n_samples,
          n_features=n_features,
          random_state=random_state,
      )
      input_data = InputData(
          idx=np.arange(0, len(synthetic_data[1])),
          features=synthetic_data[0],
          target=synthetic_data[1],
          task=Task(TaskTypesEnum.classification),
          data_type=DataTypesEnum.table,
      )
      return input_data
  
imports: import random
  from copy import deepcopy
  from typing import Tuple
  import numpy
  from sklearn.datasets import make_classification
  from sklearn.metrics import roc_auc_score
  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 3 for calling a global function with example usage: get_synthetic_input_data(n_samples, n_features, random_state) and returns: input_data