code: def random_sklearn_dataset(
      num_samples, vocab_size=60, num_classes=2, multi_label=False
  ):
      x = sparse.random(
          num_samples, vocab_size, density=0.15, format="csr"
      )
  
      if multi_label:
          y = sparse.random(
              num_samples, num_classes, density=0.5, format="csr"
          )
          y.data[np.s_[:]] = 1
          y = y.astype(int)
      else:
          y = np.random.randint(0, high=num_classes, size=x.shape[0])
  
      return SklearnDataset(x, y)
  
imports: import string
  import numpy
  from collections import Counter
  from scipy import sparse
  from sklearn.datasets import fetch_20newsgroups
  from small_text.data.datasets import SklearnDataset, TextDataset
  from small_text.data.sampling import _get_class_histogram
  from small_text.utils.labels import csr_to_list, list_to_csr
label_desc: Use image node_id 5 for calling a global function with example usage: random_sklearn_dataset(num_samples, vocab_size, num_classes, multi_label) and returns: SklearnDataset