class_docstr: VQA V2, raw images, 10k question vocab, 3k answer label.
class_docstr_tok: ["VQA","V2",",","raw","images",",","10k","question","vocab",",","3k","answer","label","."]
code: def example_reading_spec(self):
      data_fields, data_items_to_decoders = super(
          ImageVqav2Tokens10kLabels3k, self
      ).example_reading_spec()
      data_fields["image/image_id"] = tf.FixedLenFeature((), tf.int64)
      data_fields["image/question_id"] = tf.FixedLenFeature(
          (), tf.int64
      )
      data_fields["image/question"] = tf.FixedLenSequenceFeature(
          (), tf.int64, allow_missing=True
      )
      data_fields["image/answer"] = tf.FixedLenSequenceFeature(
          (), tf.int64, allow_missing=True
      )
  
      slim = contrib.slim()
      data_items_to_decoders[
          "question"
      ] = slim.tfexample_decoder.Tensor("image/question")
      data_items_to_decoders["targets"] = slim.tfexample_decoder.Tensor(
          "image/answer"
      )
      return data_fields, data_items_to_decoders
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import base64
  import csv
  import json
  import os
  import random
  import sys
  import tarfile
  import zipfile
  import numpy
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import image_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import vqa_utils
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import metrics
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 9 for calling the ImageVqav2Tokens10kLabels3k obj's underlying member method code with example usage: obj.example_reading_spec() and returns: data_fields,  data_items_to_decoders