code: def get_input_tensors(batch_size, tf_records, num_repeats=1,
                        shuffle_records=True, shuffle_examples=True,
                        shuffle_buffer_size=None,
                        filter_amount=0.05, random_rotation=True):
      
      print("Reading tf_records from {} inputs".format(len(tf_records)))
      dataset = read_tf_records(
          batch_size,
          tf_records,
          num_repeats=num_repeats,
          shuffle_records=shuffle_records,
          shuffle_examples=shuffle_examples,
          shuffle_buffer_size=shuffle_buffer_size,
          filter_amount=filter_amount,
          interleave=True)
      dataset = dataset.filter(lambda t: tf.equal(tf.shape(t)[0], batch_size))
      dataset = dataset.map(
          functools.partial(batch_parse_tf_example, batch_size))
      if random_rotation:
          dataset = dataset.map(_random_rotation_pyfunc)
  
      return dataset.make_one_shot_iterator().get_next()
docs: Read tf.Records and prepare them for ingestion by dual_net.
  
      See `read_tf_records` for parameter documentation.
  
      Returns a dict of tensors (see return value of batch_parse_tf_example)