class_docstr: Distributed optimizer, for all data types (fp16, bf16, and fp32).
  
  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.
docstr: The state dict contains all non-DP-rank-dependent (i.e., non-parameter-
  related) optimizer variables. The returned state dict can be stored in
  the standard model/RNG checkpoint file. The parameter and dependent
  optimizer state (e.g., exp_avg, exp_avg_sq) are stored in a separate
  checkpoint file by calling 'save_parameter_state()'.
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code: def state_dict(self):
      """
      The state dict contains all non-DP-rank-dependent (i.e., non-parameter-
      related) optimizer variables. The returned state dict can be stored in
      the standard model/RNG checkpoint file. The parameter and dependent
      optimizer state (e.g., exp_avg, exp_avg_sq) are stored in a separate
      checkpoint file by calling 'save_parameter_state()'.
      """
  
      state_dict = {}
  
      # Optimizer state (do not store parameter state here).
      state_dict["optimizer"] = {
          k: v
          for k, v in self.optimizer.state_dict().items()
          if k != "state"
      }
      for param_group in state_dict["optimizer"]["param_groups"]:
          del param_group["params"]
  
      # Grad scaler state.
      if self.grad_scaler:
          state_dict["grad_scaler"] = self.grad_scaler.state_dict()
  
      return state_dict
  
imports: from apex.optimizers import FusedAdam
  import math
  import torch
  from megatron import get_args
  from megatron import get_timers
  from megatron import print_rank_0
  from megatron.core import mpu, tensor_parallel
  from .optimizer import MixedPrecisionOptimizer, _zero_grad_group_helper
  from .utils import shard_buffer
label_desc: Use image node_id 10 for calling the DistributedOptimizer obj's underlying member method code with example usage: obj.state_dict() and returns: state_dict