class_docstr: Float16 optimizer wrapper for half precision (fp16 and bf16) data types.
  This optimizer wrapper holds main parameters and gradients in fp32 to support
  stable convergence.
  
  Arguments:
      optimizer: base optimizer such as Adam or SGD.
      fp32_grad_accum: to enable the use of fp32 in gradient accumulation and allreduce.
      contiguous_grad_bucket: to enable allocating the master gradients in the 
          contiguous memory space to reduce memory fragmentation.
      async_grad_allreduce: enable asynchronous gradient allreduce that is executed
          along with the training step backprop.
code: def copy_model_grads_to_main_grads(self):
      # This only needs to be done for the float16 group.
      for model_group, main_group in zip(
          self.float16_groups, self.fp32_from_float16_groups
      ):
          for model_param, main_param in zip(model_group, main_group):
              if model_param.grad is not None:
                  main_param.grad = model_param.grad.float()
  
              # Safe to deallocate model's grad after copying.
              # (If using contiguous buffers, main_grad's memory should
              # persist and therefore should not be deallocated.)
              model_param.grad = None
  
imports: from contextlib import contextmanager
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the MainParamsOptimizerWrapper obj's underlying member method code with example usage: obj.copy_model_grads_to_main_grads() without return types