text: import cupy as cp
  
  import dask
  
  from sklearn.feature_extraction.text import HashingVectorizer
  
  from cuml.dask.common import to_sparse_dask_array
  
  from sklearn.datasets import fetch_20newsgroups
  
  
  def load_text_corpus(client):
  
      categories = ['alt.atheism', 'soc.religion.christian',
                    'comp.graphics', 'sci.med']
      twenty_train = fetch_20newsgroups(subset='train',
                                        categories=categories,
                                        shuffle=True,
                                        random_state=42)
  
      hv = HashingVectorizer(alternate_sign=False, norm=None)
  
      xformed = hv.fit_transform(twenty_train.data).astype(cp.float32)
  
      X = to_sparse_dask_array(xformed, client)
  
      y = dask.array.from_array(twenty_train.target, asarray=False,
                                fancy=False).astype(cp.int32)
  
      return X, y
  