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 _set_overflow_buffer(self, half_dtype):
      if half_dtype == torch.float16:
          if self._dummy_overflow_buf is None:
              self._dummy_overflow_buf = torch.cuda.IntTensor([0])
          else:
              self._dummy_overflow_buf.fill_(0)
  
imports: from contextlib import contextmanager
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling the MainParamsOptimizerWrapper obj's underlying member method code with example usage: obj._set_overflow_buffer(half_dtype) without return types