class_docstr: Base class for both the float-16 and the distributed optimizer.
  
  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.
      params_dtype: used by distributed optimizer.
      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):
      # Copy gradients from model params to main params.
      timers("optimizer-copy-to-main-grad", log_level=1).start(
          barrier=args.barrier_with_L1_time
      )
      self._copy_model_grads_to_main_grads()
      timers("optimizer-copy-to-main-grad").stop()
  
      # Do unscale, check for inf, and update grad scaler only for
      # the case that grad scaler is provided.
      if self.grad_scaler:
          # Unscale and check for inf/nan.
          timers("optimizer-unscale-and-check-inf", log_level=1).start(
              barrier=args.barrier_with_L1_time
          )
          found_inf_flag = self._unscale_main_grads_and_check_for_nan()
          timers("optimizer-unscale-and-check-inf").stop()
  
          # We are done with scaling gradients
          # so we can update the loss scale.
          self.grad_scaler.update(found_inf_flag)
  
          # If we found inf/nan, skip the update.
          if found_inf_flag:
              return False, None, None
  
      # Clip the main gradients.
      timers("optimizer-clip-main-grad", log_level=1).start(
          barrier=args.barrier_with_L1_time
      )
      grad_norm = None
      if self.clip_grad > 0.0:
          grad_norm = self.clip_grad_norm(
              self.clip_grad, self.check_for_nan_in_grad
          )
      timers("optimizer-clip-main-grad").stop()
  
      # Count the zeros in the grads.
      timers("optimizer-count-zeros", log_level=1).start(
          barrier=args.barrier_with_L1_time
      )
      num_zeros_in_grad = (
          self.count_zeros() if self.log_num_zeros_in_grad else None
      )
      timers("optimizer-count-zeros").stop()
  
      # Step the optimizer.
      timers("optimizer-inner-step", log_level=1).start(
          barrier=args.barrier_with_L1_time
      )
      self.optimizer.step()
      timers("optimizer-inner-step").stop()
  
      # Update params from main params.
      timers("optimizer-copy-main-to-model-params", log_level=1).start(
          barrier=args.barrier_with_L1_time
      )
      self._copy_main_params_to_model_params()
      timers("optimizer-copy-main-to-model-params").stop()
  
      # Successful update.
      return True, grad_norm, num_zeros_in_grad
  
imports: from abc import ABC
  from abc import abstractmethod
  from apex.multi_tensor_apply import multi_tensor_applier
  import amp_C
  import torch
  from megatron import get_timers
  from megatron import print_rank_0
  from megatron.core import mpu, tensor_parallel
  from megatron.model import Float16Module
  from megatron.model.module import param_is_not_shared
  from .clip_grads import clip_grad_norm_fp32, count_zeros_fp32
label_desc: Use image node_id 5 for calling the MixedPrecisionOptimizer obj's underlying member method code with example usage: obj.step(args, timers) and returns: True,  grad_norm,  num_zeros_in_grad, False,  None,  None