code: def gauss_quantiles_dataset(
      samples_amount: int,
      features_amount: int,
      classes_amount: int,
      full_shuffle=True,
      **kwargs,
  ):
      """Generates a random dataset for n-class classification problem
      based on multi-dimensional gaussian distribution quantiles
      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
          full_shuffle: if ``True`` then all features and samples will be shuffled
          kwargs: Optional['gauss_params'] mean and covariance values of the distribution
  
      Returns:
          array: features and target as numpy-arrays
      """
  
      if "gauss_params" in kwargs:
          mean, cov = kwargs["gauss_params"]
      else:
          mean, cov = None, 1.0
  
      features, target = datasets.make_gaussian_quantiles(
          n_samples=samples_amount,
          n_features=features_amount,
          n_classes=classes_amount,
          shuffle=full_shuffle,
          mean=mean,
          cov=cov,
      )
      return features, target
  
imports: from typing import Dict
  import numpy
  from sklearn import datasets
label_desc: Use image node_id 3 for calling a global function with example usage: gauss_quantiles_dataset(samples_amount, features_amount, classes_amount, full_shuffle) and returns: features,  target