code: def setUp(self):
      # fetch the dataset using scikit-learn
      categories = [
          "alt.atheism",
          "soc.religion.christian",
          "comp.graphics",
          "sci.med",
      ]
      from sklearn.datasets import fetch_20newsgroups
  
      train_b = fetch_20newsgroups(
          subset="train",
          categories=categories,
          shuffle=True,
          random_state=42,
      )
      test_b = fetch_20newsgroups(
          subset="test",
          categories=categories,
          shuffle=True,
          random_state=42,
      )
      print("size of training set: %s" % (len(train_b["data"])))
      print("size of validation set: %s" % (len(test_b["data"])))
      print("classes: %s" % (train_b.target_names))
      x_train = train_b.data
      y_train = train_b.target
      x_test = test_b.data
      y_test = test_b.target
  
      # convert to string labels
      y_train = [train_b.target_names[y] for y in y_train]
      y_test = [train_b.target_names[y] for y in y_test]
  
      # setup
      self.trn = (x_train, y_train)
      self.val = (x_test, y_test)
      # self.classes = train_b.target_names
      self.classes = []
  
file_path: ktrain/tests/test_transformers.py
imports: from unittest import TestCase, main, skip
  import IPython
  import numpy
  import testenv
  import ktrain
  from ktrain import text
  from ktrain.imports import ACC_NAME, VAL_ACC_NAME
label_desc: Use image node_id 1 for calling the TestTransformers obj's underlying member method code with example usage: obj.setUp() without return types