code: def get_regression_dataset(
      features_options, samples_amount=250, features_amount=5
  ):
      """
      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 features_options: The dictionary containing features options in key-value
      format:
          - informative: the amount of informative features;
          - bias: bias term in the underlying linear model;
      :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 = regression_dataset(
          samples_amount=samples_amount,
          features_amount=features_amount,
          features_options=features_options,
          n_targets=1,
          noise=0.0,
          shuffle=True,
      )
  
      # 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_train, x_test, y_train, y_test = train_test_split(
          x_data, y_data, test_size=0.3
      )
  
      return x_train, y_train, x_test, y_test
  
file_path: FEDOT/examples/simple/regression/regression_with_tuning.py
imports: from datetime import timedelta
  import numpy
  from golem.core.tuning.sequential import SequentialTuner
  from sklearn.metrics import mean_absolute_error
  from sklearn.model_selection import train_test_split
  from examples.simple.regression.regression_pipelines import regression_ransac_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 RegressionMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import set_random_seed
  from fedot.utilities.synth_dataset_generator import regression_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: get_regression_dataset(features_options, samples_amount, features_amount) and returns: x_train,  y_train,  x_test,  y_test