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 _get_state(self):
      if hasattr(self, "optimizer"):
          return self.optimizer.state
      else:
          return []
  
imports: from contextlib import contextmanager
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 18 for calling the MainParamsOptimizerWrapper obj's underlying member method code with example usage: obj._get_state() and returns: self, list