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 state_dict(self):
      state_dict = {}
      state_dict["optimizer"] = self.optimizer.state_dict()
      state_dict[
          "fp32_from_fp16_params"
      ] = self.fp32_from_float16_groups
      return state_dict
  
imports: from contextlib import contextmanager
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 11 for calling the MainParamsOptimizerWrapper obj's underlying member method code with example usage: obj.state_dict() and returns: state_dict