class_docstr: A wrapper for the optimizer to enable gradient accumulation by skipping the steps
  before accumulation size is reached.
  
  Args:
      optim (:class:`torch.optim.Optimizer`): Your optimizer object for gradient accumulation.
      accumulate_size (int): The number of steps to accumulate gradients.
      model (:class:`torch.nn.Module`):
          Your model object to check if it is DistributedDataParallel for special handling of no_sync() context.
docstr: Set all gradients to zero.
  
  Args:
      *args: positional arguments for the optimizer wrapped
      **kwargs: keyword arguments for the optimizer wrapped
code: def zero_grad(self, *args, **kwargs) -> None:
      """
      Set all gradients to zero.
  
      Args:
          *args: positional arguments for the optimizer wrapped
          **kwargs: keyword arguments for the optimizer wrapped
      """
  
      if self.accumulate_step == 0:
          self.optim.zero_grad(*args, **kwargs)
  
imports: from typing import Any, Iterable, Tuple, Union
  import torch.nn
  from torch import Tensor
  from torch.nn.parallel.distributed import DistributedDataParallel
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils.data import DataLoader
  from colossalai.interface import OptimizerWrapper
  from colossalai.legacy.engine import BaseGradientHandler
  from colossalai.utils import conditional_context
label_desc: Use image node_id 2 for calling the GradAccumOptimizer obj's underlying member method code with example usage: obj.zero_grad() without return types