class_docstr: Recognizing Textual Entailment classification problems.
code: def example_generator(self, filename):
      label_list = self.class_labels(data_dir=None)
      for idx, line in enumerate(tf.gfile.Open(filename, "rb")):
          if idx == 0:
              continue  # skip header
          line = text_encoder.to_unicode_utf8(line.strip())
          _, s1, s2, l = line.split("\t")
          inputs = [s1, s2]
          l = label_list.index(l)
          yield {"inputs": inputs, "label": l}
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import zipfile
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 7 for calling the RTE obj's underlying member method code with example usage: obj.example_generator(filename) without return types