code: def backward(self, loss: th.Tensor):
      if self.use_fp16:
          loss_scale = 2**self.lg_loss_scale
          (loss * loss_scale).backward()
      else:
          loss.backward()
  
imports: import numpy
  import torch
  import torch.nn
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from .None import logger
label_desc: Use image node_id 3 for calling the MixedPrecisionTrainer obj's underlying member method code with example usage: obj.backward(loss) without return types