class_docstr: Optimizer used for ZeRO-1 and ZeRO-2.
docstr: Set parameter gradients to zero. If set_to_none = True, gradient
  will be set to None to save memory.
  
  :param set_to_none: Whether set the gradient to None. Default value is True.
  :type set_to_none: bool
docstr_tok: ["Set","parameter","gradients","to","zero",".","If","set_to_none","=","True",",","gradient","will","be","set","to","None","to","save","memory",".",":","param","set_to_none",":","Whether","set","the","gradient","to","None",".","Default","value","is","True",".",":","type","set_to_none",":","bool"]
code: def zero_grad(self, set_to_none=True):
      """
      Set parameter gradients to zero. If set_to_none = True, gradient
      will be set to None to save memory.
  
      :param set_to_none: Whether set the gradient to None. Default value is True.
      :type set_to_none: bool
      """
      if self.mixed_precision_mixin is not None:
          self.mixed_precision_mixin.pre_zero_grad()
      for _, param_group in self._working_param_groups.items():
          for param in param_group:
              if set_to_none:
                  param.grad = None
              else:
                  if param.grad is not None:
                      param.grad.detach()
                      param.grad.zero_()
  
imports: import copy
  from contextlib import contextmanager
  from functools import partial
  from typing import Dict, Iterator, List, Optional, Tuple
  import torch
  import torch.distributed
  import torch.nn
  from torch import Tensor, inf
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from torch.distributed import ProcessGroup
  from torch.optim import Optimizer
  import colossalai.utils.device
  from colossalai.amp.naive_amp.mixed_precision_mixin import BF16MixedPrecisionMixin, FP16MixedPrecisionMixin, MixedPrecisionMixin
  from colossalai.interface import OptimizerWrapper
  from colossalai.logging import get_dist_logger
  from colossalai.tensor.moe_tensor.api import is_moe_tensor
  from colossalai.utils.device import IS_NPU_AVAILABLE, get_current_device
  from ._utils import calculate_global_norm_from_list, flatten, has_inf_or_nan, release_param_grad, sync_tensor
  from .bookkeeping import BucketStore, GradientStore, ParameterStore
label_desc: Use image node_id 15 for calling the LowLevelZeroOptimizer obj's underlying member method code with example usage: obj.zero_grad(set_to_none) without return types