code: def forward_and_backward_pass(
      self, mod, inputs, collect_outputs=True
  ):
      cloned_inputs = clone_inputs(inputs)
      self.optimizer_zero_grad(mod)
      with self.autocast(**self.autocast_arg):
          pred = mod(*cloned_inputs)
          if isinstance(pred, tuple):
              pred = pred[0]
          loss = self.compute_loss(pred)
      self.grad_scaler.scale(loss).backward()
      self.optimizer_step()
      if collect_outputs:
          return collect_results(mod, pred, loss, cloned_inputs)
      return None
  
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 14 for calling the TimmRunner obj's underlying member method code with example usage: obj.forward_and_backward_pass(mod, inputs, collect_outputs) and returns: None, collect_results