code: def get_small_classification_dataset():
      """Function returns features and target for train and test classification models"""
      features_options = {
          "informative": 1,
          "redundant": 0,
          "repeated": 0,
          "clusters_per_class": 1,
      }
      x_train, y_train, x_test, y_test = get_classification_dataset(
          features_options=features_options,
          samples_amount=70,
          features_amount=4,
          classes_amount=2,
      )
      y_train = y_train.reshape((-1, 1))
      y_test = y_test.reshape((-1, 1))
      # Define regression task
      task = Task(TaskTypesEnum.classification)
  
      # Prepare data to train 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=None,
          task=task,
          data_type=DataTypesEnum.table,
      )
  
      return train_input, predict_input, y_test
  
imports: import os
  import numpy
  import pytest
  from examples.simple.classification.classification_with_tuning import get_classification_dataset
  from examples.simple.regression.regression_with_tuning import get_regression_dataset
  from examples.simple.time_series_forecasting.gapfilling import generate_synthetic_data
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_imbalanced_class import ResampleImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import ImputationImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import CutImplementation, LaggedTransformationImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  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.operation_types_repository import OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.preprocessing.data_types import TYPE_TO_ID
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_only_categorical_features
label_desc: Use image node_id 2 for calling a global function with example usage: get_small_classification_dataset() and returns: train_input,  predict_input,  y_test