code: def get_vqa_knowledge():
      """Concat title passage and document together to form knowledge."""
  
      def get_vqa_knowledge_fn(data):
          """Prepare Knowledge by concating hierarchy, passage and first-paragraph."""
  
          questions = data["question/answers"]["question_text"]
          answers = tf.strings.reduce_join(
              data["question/answers"]["top_answers"] + ", ", axis=1
          )
          q_prefix = tf.repeat(
              ["Question: "], repeats=tf.shape(questions)[0]
          )
          a_prefix = tf.repeat(
              [" Answer: "], repeats=tf.shape(questions)[0]
          )
          sep_token = tf.repeat(
              [" <extra_id_99> "], repeats=tf.shape(questions)[0]
          )
          knowledges = tf.strings.join(
              [q_prefix, questions, a_prefix, answers, sep_token]
          )
          # data['knowledge'] = tf.strings.reduce_join(knowledges, axis=0)
          data["knowledge"] = tf.strings.reduce_join(knowledges, axis=0)
          return data
  
      return get_vqa_knowledge_fn
  
file_path: scenic/scenic/projects/knowledge_visual_language/data/vqa_table_dataset.py
imports: import functools
  from typing import Optional
  from absl import logging
  import jax
  import jax.numpy
  import ml_collections
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.dataset_lib.big_transfer import builder
  from scenic.dataset_lib.big_transfer import registry
  from scenic.dataset_lib import web_image_text_dataset
  from scenic.projects.knowledge_visual_language.data import data_utils
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: get_vqa_knowledge() and returns: get_vqa_knowledge_fn, data