code: 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
  
imports: from sklearn.datasets import fetch_20newsgroups
  from cuml.dask.common import to_sparse_dask_array
  from sklearn.feature_extraction.text import HashingVectorizer
  import dask
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: load_text_corpus(client) and returns: X,  y