code: def get_classification_dataset(
      features_options,
      samples_amount=250,
      features_amount=5,
      classes_amount=2,
      weights=None,
  ):
      """
      Prepares four numpy arrays with different scale features and target
      :param samples_amount: Total amount of samples in the resulted dataset.
      :param features_amount: Total amount of features per sample.
      :param classes_amount: The amount of classes in the dataset.
      :param features_options: The dictionary containing features options in key-value
      :param weights: The proportions of samples assigned to each class. If None, then classes are balanced.
      format:
          - informative: the amount of informative features;
          - redundant: the amount of redundant features;
          - repeated: the amount of features that repeat the informative features;
          - clusters_per_class: the amount of clusters for each class;
      :return x_data_train: features to train
      :return y_data_train: target to train
      :return x_data_test: features to test
      :return y_data_test: target to test
      """
  
      x_data, y_data = classification_dataset(
          samples_amount=samples_amount,
          features_amount=features_amount,
          classes_amount=classes_amount,
          features_options=features_options,
          weights=weights,
      )
  
      # Changing the scale of the data
      for i, coeff in zip(
          range(0, features_amount),
          np.random.randint(1, 100, features_amount),
      ):
          # Get column
          feature = np.array(x_data[:, i])
  
          # Change scale for this feature
          rescaled = feature * coeff
          x_data[:, i] = rescaled
  
      # Train and test split
      (
          x_data_train,
          x_data_test,
          y_data_train,
          y_data_test,
      ) = train_test_split(x_data, y_data, test_size=0.3)
  
      return x_data_train, y_data_train, x_data_test, y_data_test
  
file_path: FEDOT/examples/simple/classification/classification_with_tuning.py
imports: import numpy
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from examples.simple.classification.classification_pipelines import classification_random_forest_pipeline
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import set_random_seed
  from fedot.utilities.synth_dataset_generator import classification_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: get_classification_dataset(features_options, samples_amount, features_amount, classes_amount, weights) and returns: x_data_train,  y_data_train,  x_data_test,  y_data_test