code: def setUp(self) -> None:
      tf.random.set_seed(42)
      np.random.seed(42)
      data = [("I love dogs.", "Dogs."), ("I love cats.", "Cats.")]
      tokenizer_encoder = BertTokenizer.from_pretrained(
          "bert-base-uncased"
      )
      self.preprocessor = BertPreprocessor(nlp=English())
      data_prep = [self.preprocessor(d) for d in data]
      tokenizer_decoder = KerasTokenizer(lower=False, filters="")
      tokenizer_decoder.fit([d[1] for d in data_prep])
      self.vectorizer = BertVectorizer(
          tokenizer_encoder=tokenizer_encoder,
          tokenizer_decoder=tokenizer_decoder,
          max_output_len=10,
      )
      batch_generator = DatasetGenerator(2, rank=3)
      data_vecs = [self.vectorizer(d) for d in data_prep]
      self.dataset = batch_generator(lambda: data_vecs)
      self.loss_func = masked_crossentropy
  
file_path: headliner/tests/test_bert_training.py
imports: import unittest
  import numpy
  import tensorflow
  from spacy.lang.en import English
  from transformers import BertTokenizer
  from headliner.losses import masked_crossentropy
  from headliner.model.bert_summarizer import BertSummarizer
  from headliner.preprocessing.bert_preprocessor import BertPreprocessor
  from headliner.preprocessing.bert_vectorizer import BertVectorizer
  from headliner.preprocessing.dataset_generator import DatasetGenerator
  from headliner.preprocessing.keras_tokenizer import KerasTokenizer
label_desc: Use image node_id 1 for calling the TestBertTraining obj's underlying member method code with example usage: obj.setUp() without return types