class_docstr: Task for Question Answering.
  
  Attributes:
      model:
          1. Model name
          2. Model name on Backprop's qa endpoint
          3. Model object that implements the qa task
      local (optional): Run locally. Defaults to False
      api_key (optional): Backprop API key for non-local inference
      device (optional): Device to run inference on. Defaults to "cuda" if available.
docstr: Returns default optimizer for Q&A (AdaFactor, learning rate 1e-3)
docstr_tok: ["Returns","default","optimizer","for","Q","&","A","(","AdaFactor",",","learning","rate","1e-3",")"]
code: def configure_optimizers(self):
      """
      Returns default optimizer for Q&A (AdaFactor, learning rate 1e-3)
      """
      return Adafactor(
          params=self.model.parameters(),
          lr=1e-3,
          scale_parameter=False,
          relative_step=False,
      )
  
file_path: backprop/backprop/tasks/qa.py
imports: from typing import List, Tuple, Union, Dict
  from .base import Task
  from backprop.models import BaseModel, AutoModel
  from transformers.optimization import Adafactor
  from backprop.utils.datasets import TextToTextDataset
  import requests
label_desc: Use image node_id 5 for calling the QA obj's underlying member method code with example usage: obj.configure_optimizers() and returns: Adafactor