docstr: Clip gradients (if needed) and step the base optimizer.
  Always return successful since there is no overflow.
docstr_tok: ["Clip","gradients","(","if","needed",")","and","step","the","base","optimizer",".","Always","return","successful","since","there","is","no","overflow","."]
code: def step(self, args, timers):
      """Clip gradients (if needed) and step the base optimizer.
      Always return successful since there is no overflow."""
  
      # Copy main_grads to grads.
      timers("optimizer-copy-to-main-grad", log_level=1).start(
          barrier=args.barrier_with_L1_time
      )
      if self.params_have_main_grad:
          for param_group in self.optimizer.param_groups:
              for param in param_group["params"]:
                  param.grad = param.main_grad
  
      timers("optimizer-copy-to-main-grad").stop()
  
      # Clip 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()
  
      # Update parameters.
      timers("optimizer-inner-step", log_level=1).start(
          barrier=args.barrier_with_L1_time
      )
      self.optimizer.step()
      timers("optimizer-inner-step").stop()
  
      # No overflow for FP32 optimizer.
      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 4 for calling the FP32Optimizer obj's underlying member method code with example usage: obj.step(args, timers) and returns: True,  grad_norm,  num_zeros_in_grad