class_docstr: Distributed optimizer, for all data types (fp16, bf16, and fp32).
  
  Arguments:
      optimizer: base optimizer such as Adam or SGD
      clip_grad: clip gradeints with this global L2 norm. Note
          that clipping is ignored if clip_grad == 0
      log_num_zeros_in_grad: return number of zeros in the gradients.
      check_for_nan_in_grad: check if gradients have a NaN.
      params_have_main_grad: flag indicating if parameters have
          a `main_grad` field. If this is set, we are assuming
          that the model parameters are store in the `main_grad`
          field instead of the typical `grad` field. This happens
          for the DDP cases where there is a continuous buffer
          holding the gradients. For example for bfloat16, we want
          to do gradient accumulation and all-reduces in float32
          and as a result we store those gradients in the main_grad.
          Note that main grad is not necessarily in float32.
      fp16: if true, the model is running in fp16.
      bf16: if true, the model is running in bfloat16.
      grad_scaler: used for scaling gradients. Note that this can be
          None. This case happens when `bf16 = True` and we don't
          use any loss scale. Note that for `bf16 = True`, we can have
          a constnat gradient scaler. Also for `bf16 = False`, we
          always require a grad scaler.
      models: list of models (i.e., the virtual pipelining models). This
          is used by the distributed optimizer for mapping parameters.
code: def step(self, args, timers):
      (
          self.update_successful,
          grad_norm,
          num_zeros_in_grad,
      ) = super().step(args, timers)
  
      # Reset metadata needed to track results of all-gathers.
      self.all_gather_handles = []
      self.param_buffer_copied = []
  
      # If not overlapping all-gather for parameters, launch synchronous all-gather
      # communication calls here.
      if not self.overlap_param_gather:
          timers("params-all-gather", log_level=1).start(
              barrier=args.barrier_with_L1_time
          )
          for all_gather_handle_index in range(
              self.num_all_gather_handles
          ):
              self._dispatch_gather_model_params(
                  all_gather_handle_index
              )
          timers("params-all-gather").stop()
  
      return self.update_successful, grad_norm, num_zeros_in_grad
  
imports: from apex.optimizers import FusedAdam
  import math
  import torch
  from megatron import get_args
  from megatron import get_timers
  from megatron import print_rank_0
  from megatron.core import mpu, tensor_parallel
  from .optimizer import MixedPrecisionOptimizer, _zero_grad_group_helper
  from .utils import shard_buffer
label_desc: Use image node_id 26 for calling the DistributedOptimizer obj's underlying member method code with example usage: obj.step(args, timers) and returns: self,  grad_norm,  num_zeros_in_grad