code: def backward(self, loss: torch.Tensor, model: nn.Module):
      # See: https://github.com/huggingface/accelerate/blob/main/src/accelerate/utils/deepspeed.py
      # runs backpropagation and handles mixed precision
      model.backward(loss)
  
      # Deepspeed's `engine.step` performs the following operations:
      # - gradient accumulation check
      # - gradient clipping
      # - optimizer step
      # - zero grad
      # - checking overflow
      # - lr_scheduler step (only if engine.lr_scheduler is not None)
      model.step()
  
imports: import logging
  import os
  import warnings
  from typing import Any, Dict, List, Mapping, Optional, Tuple, TYPE_CHECKING, Union
  import deepspeed
  import deepspeed.comm
  import torch
  from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
  from packaging import version
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from ludwig.constants import MIN_POSSIBLE_BATCH_SIZE
  from ludwig.distributed.ddp import DDPStrategy
  from ludwig.modules.optimization_modules import get_optimizer_class_and_kwargs
  from ludwig.utils.checkpoint_utils import Checkpoint
  from ludwig.utils.model_utils import extract_tensors, replace_tensors
label_desc: Use image node_id 6 for calling the DeepSpeedStrategy obj's underlying member method code with example usage: obj.backward(loss, model) without return types