code: def data_gen_for_mcq():
      prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
      choice0 = "It is eaten with a fork and a knife."
      choice1 = "It is eaten while held in the hand."
      tokenizer = transformers.BertTokenizer.from_pretrained(
          "bert-base-uncased"
      )
      encoding = tokenizer(
          [prompt, prompt],
          [choice0, choice1],
          return_tensors="pt",
          padding=True,
      )
      encoding = {k: v.unsqueeze(0) for k, v in encoding.items()}
      return encoding
  
imports: import torch
  import transformers
  from ..registry import ModelAttribute, model_zoo
label_desc: Use image node_id 4 for calling a global function with example usage: data_gen_for_mcq() and returns: encoding