class_docstr: Adam optimizer with ZeRO algorithm
  
  Child class of Apex DistributedFusedAdam, with optimizations for
  NeMo-Megatron.
  
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts
          defining parameter groups.
      disable_distributed_parameters (bool, optional): use standard
          data-parallel communication instead of ZeRO.
          (default: False)
      **kwargs: keyword arguments to pass to Apex
          DistributedFusedAdam.
docstr: Attempt to launch gradient synchronization
docstr_tok: ["Attempt","to","launch","gradient","synchronization"]
code: def try_grad_sync(self, params: Iterable[torch.nn.Parameter]) -> None:
      """Attempt to launch gradient synchronization"""
  
      def is_grad_copy_enabled(param: torch.nn.Parameter) -> bool:
          return not getattr(
              param, "_disable_greedy_grad_copy", False
          ) and not getattr(param, "_disable_overlap_grad_sync", False)
  
      params = list(filter(is_grad_copy_enabled, params))
      for p in params:
          self._grad_copy(p)
      self._try_start_bucket_grad_sync(params=params)
  
imports: import collections
  from typing import Callable, Dict, Iterable, Optional, Union
  import torch
  from apex.contrib.optimizers.distributed_fused_adam import DistributedFusedAdam, _disable_pre_forward_hook, _multi_tensor_copy
  from megatron.core import parallel_state
  from megatron.core.dist_checkpointing.dict_utils import dict_list_map_inplace
  from megatron.core.dist_checkpointing.mapping import ShardedTensor
  from megatron.core.dist_checkpointing.optimizer import get_param_id_to_sharded_param_map, optim_state_to_sharding_state
  from nemo.utils import str_to_dtype
label_desc: Use image node_id 8 for calling the MegatronDistributedFusedAdam obj's underlying member method code with example usage: obj.try_grad_sync(params) and returns: unknown