code: def compute_loss(self, pred):
      # High loss values make gradient checking harder, as small changes in
      # accumulation order upsets accuracy checks.
      return reduce_to_scalar_loss(pred)
  
imports: import importlib
  import logging
  import os
  import re
  import subprocess
  import sys
  import warnings
  import torch
  from common import BenchmarkRunner, download_retry_decorator, main
  from torch._dynamo.testing import collect_results, reduce_to_scalar_loss
  from torch._dynamo.utils import clone_inputs
label_desc: Use image node_id 11 for calling the TimmRunner obj's underlying member method code with example usage: obj.compute_loss(pred) and returns: reduce_to_scalar_loss