class_docstr: Initialises a T5 model that has been finetuned on qa, summarisation and emotion detection.
  
  Attributes:
      model_path: path to an appropriate T5 model on huggingface (kiri-ai/t5-base-qa-summary-emotion)
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      device: Device for model. Defaults to "cuda" if available.
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code: def qa(self, question, context, prev_qa: List[Tuple[str, str]] = []):
      if isinstance(question, list):
          # Must have a consistent amount of examples
          assert len(question) == len(context)
          if len(prev_qa) != 0:
              assert len(question) == len(prev_qa)
          else:
              prev_qa = [prev_qa] * len(question)
  
          # Process according to the model used
          input_text = [
              self.process_qa(q, c, p)
              for q, c, p in zip(question, context, prev_qa)
          ]
      else:
          if len(prev_qa) != 0:
              prev_qa = prev_qa[0]
          input_text = self.process_qa(question, context, prev_qa)
      return self.generate(
          input_text, do_sample=False, min_length=1, max_length=96
      )
  
file_path: backprop/backprop/models/t5_qa_summary_emotion/model.py
imports: from typing import List, Tuple, Dict
  import torch
  from backprop.models import HFSeq2SeqTGModel
label_desc: Use image node_id 7 for calling the T5QASummaryEmotion obj's underlying member method code with example usage: obj.qa(question, context, prev_qa) and returns: self