code: def classification_dataset(
      samples_amount: int,
      features_amount: int,
      classes_amount: int,
      features_options: Dict,
      noise_fraction: float = 0.1,
      full_shuffle: bool = True,
      weights: list = None,
  ):
      """Generates a random dataset for ``n-class`` classification problem
      using scikit-learn API.
  
      Args:
          samples_amount: Total amount of samples in the resulted dataset.
          features_amount: Total amount of features per sample.
          classes_amount: The amount of classes in the dataset.
          features_options: The dictionary containing features options in key-value format
  
              .. details:: possible ``features_options`` variants:
  
                  - ``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
  
          noise_fraction: the fraction of noisy labels in the dataset
          full_shuffle: if true then all features and samples will be shuffled
          weights: The proportions of samples assigned to each class. If None, then classes are balanced
  
      Returns:
          array: features and target as numpy-arrays
      """
  
      features, target = datasets.make_classification(
          n_samples=samples_amount,
          n_features=features_amount,
          n_informative=features_options["informative"],
          n_redundant=features_options["redundant"],
          n_repeated=features_options["repeated"],
          n_classes=classes_amount,
          n_clusters_per_class=features_options["clusters_per_class"],
          weights=weights,
          flip_y=noise_fraction,
          shuffle=full_shuffle,
      )
  
      return features, target
  
imports: from typing import Dict
  import numpy
  from sklearn import datasets
label_desc: Use image node_id 1 for calling a global function with example usage: classification_dataset(samples_amount, features_amount, classes_amount, features_options, noise_fraction, full_shuffle, weights) and returns: features,  target