class_docstr: A wrapper class for APEX optimizer and it implements apex-specific backward and clip_grad_norm
  methods
docstr: Backward pass to get all gradients
  
  Args:
      loss (torch.Tensor): Loss computed by a loss function
docstr_tok: ["Backward","pass","to","get","all","gradients","Args",":","loss","(","torch.Tensor",")",":","Loss","computed","by","a","loss","function"]
code: def backward(self, loss: Tensor):
      """Backward pass to get all gradients
  
      Args:
          loss (torch.Tensor): Loss computed by a loss function
      """
      with apex_amp.scale_loss(loss, self.optim) as scaled_loss:
          scaled_loss.backward()
  
file_path: colossalai/colossalai/legacy/amp/apex_amp/apex_amp.py
imports: import torch.nn
  from torch import Tensor
  from colossalai.interface import OptimizerWrapper
  from colossalai.legacy.utils import clip_grad_norm_fp32
label_desc: Use image node_id 1 for calling the ApexAMPOptimizer obj's underlying member method code with example usage: obj.backward(loss) without return types