code: def get_classification_data(classes_amount: int = 2):
      """Function generate synthetic dataset for classification task
  
      :param classes_amount: amount of classes to predict
  
      :return train_input: InputData for model fit
      :return predict_input: InputData for predict stage
      """
  
      # Define options for dataset with 800 objects
      features_options = {
          "informative": 2,
          "redundant": 1,
          "repeated": 1,
          "clusters_per_class": 1,
      }
      x_train, y_train, x_test, y_test = get_classification_dataset(
          features_options, 800, 4, classes_amount
      )
      y_train = y_train.reshape((-1, 1))
      y_test = y_test.reshape((-1, 1))
  
      # Define classification task
      task = Task(TaskTypesEnum.classification)
  
      # Prepare data to train and validate the model
      train_input = InputData(
          idx=np.arange(0, len(x_train)),
          features=x_train,
          target=y_train,
          task=task,
          data_type=DataTypesEnum.table,
      )
      predict_input = InputData(
          idx=np.arange(0, len(x_test)),
          features=x_test,
          target=y_test,
          task=task,
          data_type=DataTypesEnum.table,
      )
  
      return train_input, predict_input
  
imports: from itertools import product
  import numpy
  from examples.simple.classification.classification_with_tuning import get_classification_dataset
  from examples.advanced.decompose.refinement_forecast_example import get_refinement_pipeline
  from fedot.core.data.data import InputData
  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
  from fedot.preprocessing.preprocessing import DataPreprocessor
  from test.unit.tasks.test_classification import get_iris_data
  from test.unit.tasks.test_forecasting import get_ts_data
  from test.unit.preprocessing.test_preprocessors import data_with_complicated_types
label_desc: Use image node_id 4 for calling a global function with example usage: get_classification_data(classes_amount) and returns: train_input,  predict_input