class_docstr: VQA V2, image feature, 10k question vocab, 3k answer label.
class_docstr_tok: ["VQA","V2",",","image","feature",",","10k","question","vocab",",","3k","answer","label","."]
code: def feature_dimension(self):
      return 2048
  
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 2 for calling the ImageVqav2RcnnFeatureTokens10kLabels3k obj's underlying member method code with example usage: obj.feature_dimension() and returns: int