code: def test_qa_conv_batch():
      questions = [qa_example["q2_conv"], qa_example["q3_conv"]]
      ctxs = [qa_example["c"], qa_example["c"]]
      prev_qa = [
          [(qa_example["q1"], qa_example["a1"])],
          [(qa_example["q2"], qa_example["a2"])],
      ]
  
      out = qa(questions, ctxs, prev_qa=prev_qa)
      assert isinstance(out, list), "Output not a list"
      assert len(out) == 2, "Incorrect number of answers"
      assert (
          out[0] == qa_example["a2"]
      ), "Wrong answer to simple conversational qa"
      assert (
          out[1] == qa_example["a3"]
      ), "Wrong answer to simple conversational qa"
  
imports: from backprop import Emotion, ImageClassification, QA, Summarisation, TextClassification, TextGeneration, TextVectorisation
  import numpy
  import torch
  import os
label_desc: Use image node_id 6 for calling a global function with example usage: test_qa_conv_batch() without return types