code: def regression_dataset(
      samples_amount: int,
      features_amount: int,
      features_options: Dict,
      n_targets: int,
      noise: float = 0.0,
      shuffle: bool = True,
  ):
      """Generates a random dataset for regression problem using scikit-learn API.
  
      Args:
          samples_amount: total amount of samples in the resulted dataset
          features_amount: total amount of features per sample
          features_options: the dictionary containing features options in key-value format
  
              .. details:: possible ``features_options`` variants:
  
                  - ``informative`` -> the amount of informative features
                  - ``bias`` -> bias term in the underlying linear model
  
          n_targets: the amount of target variables
          noise: the standard deviation of the gaussian noise applied to the output
          shuffle: if ``True`` then all features and samples will be shuffled
  
      Returns:
          array: features and target as numpy-arrays
      """
  
      features, target = datasets.make_regression(
          n_samples=samples_amount,
          n_features=features_amount,
          n_informative=features_options["informative"],
          bias=features_options["bias"],
          n_targets=n_targets,
          noise=noise,
          shuffle=shuffle,
      )
  
      return features, target
  
imports: from typing import Dict
  import numpy
  from sklearn import datasets
label_desc: Use image node_id 2 for calling a global function with example usage: regression_dataset(samples_amount, features_amount, features_options, n_targets, noise, shuffle) and returns: features,  target