code: def step(self, closure=None):
      loss = None
      if closure is not None:
          loss = closure()
  
      for group in self.param_groups:
          for p in group["params"]:
              if p.grad is None:
                  continue
  
              state = self.state[p]
              if len(state) == 0:
                  state["integer_step"] = 0
                  state["tensor_step"] = torch.zeros(1, device=p.device)
  
              d_p = p.grad.data
              p.data.add_(-group["lr"], d_p)
              state["integer_step"] += 1
              state["tensor_step"] += 1
  
      return loss
  
imports: import os
  import json
  import argparse
  import torch
  from collections import OrderedDict
  from deepspeed.pipe import PipelineModule, LayerSpec
  from deepspeed.moe.layer import MoE
  from deepspeed.accelerator import get_accelerator
  import deepspeed.comm
label_desc: Use image node_id 3 for calling the HybridStateOptimizer obj's underlying member method code with example usage: obj.step(closure) and returns: loss