class_docstr: Float16 optimizer for fp16 and bf16 data types.
  
  Arguments:
      optimizer: base optimizer such as Adam or SGD
      clip_grad: clip gradeints with this global L2 norm. Note
          that clipping is ignored if clip_grad == 0
      log_num_zeros_in_grad: return number of zeros in the gradients.
      check_for_nan_in_grad: check if gradients have a NaN.
      params_have_main_grad: flag indicating if parameters have
          a `main_grad` field. If this is set, we are assuming
          that the model parameters are store in the `main_grad`
          field instead of the typical `grad` field. This happens
          for the DDP cases where there is a continuous buffer
          holding the gradients. For example for bfloat16, we want
          to do gradient accumulation and all-reduces in float32
          and as a result we store those gradients in the main_grad.
          Note that main grad is not necessarily in float32.
      fp16: if true, the model is running in fp16.
      bf16: if true, the model is running in bfloat16.
      grad_scaler: used for scaling gradients. Note that this can be
          None. This case happens when `bf16 = True` and we don't
          use any loss scale. Note that for `bf16 = True`, we can have
          a constnat gradient scaler. Also for `bf16 = False`, we
          always require a grad scaler.
      models: list of models (i.e., the virtual pipelining models). This
          is used by the distributed optimizer for mapping parameters.
code: def load_state_dict(self, state_dict):
      # Optimizer.
      optimizer_key = "optimizer"
      if optimizer_key not in state_dict:
          optimizer_key = "optimizer_state_dict"
          print_rank_0(
              "***WARNING*** loading optimizer from "
              "an old checkpoint ..."
          )
      self.optimizer.load_state_dict(state_dict[optimizer_key])
  
      # Grad scaler.
      if "grad_scaler" not in state_dict:
          if self.fp16:
              print_rank_0(
                  "***WARNING*** found an old checkpoint, will not "
                  "load grad scaler ..."
              )
      else:
          if self.grad_scaler:
              self.grad_scaler.load_state_dict(
                  state_dict["grad_scaler"]
              )
          else:
              print_rank_0(
                  "***WARNING*** fould the grad scaler in the "
                  "checkpoint but it is None in the class. "
                  "Skipping loading grad scaler ..."
              )
  
      # Copy data for the main params.
      fp32_from_float16_params_key = "fp32_from_fp16_params"
      if fp32_from_float16_params_key not in state_dict:
          fp32_from_float16_params_key = "fp32_from_fp16"
      for current_group, saved_group in zip(
          self.fp32_from_float16_groups,
          state_dict[fp32_from_float16_params_key],
      ):
          for current_param, saved_param in zip(
              current_group, saved_group
          ):
              current_param.data.copy_(saved_param.data)
  
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 9 for calling the Float16OptimizerWithFloat16Params obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types