code: def optimize_normal(self):
      self._log_grad_norm()
      self._anneal_lr()
      self.opt.step()
      for rate, params in zip(self.ema_rate, self.ema_params):
          update_ema(params, self.master_params, rate=rate)
  
imports: import copy
  import functools
  import os
  import blobfile
  import numpy
  import torch
  import torch.distributed
  from torch.nn.parallel.distributed import DistributedDataParallel
  from torch.optim import AdamW
  from .None import dist_util, logger
  from .fp16_util import make_master_params, master_params_to_model_params, model_grads_to_master_grads, unflatten_master_params, zero_grad
  from .nn import update_ema
  from .resample import LossAwareSampler, UniformSampler
label_desc: Use image node_id 10 for calling the TrainLoop obj's underlying member method code with example usage: obj.optimize_normal() without return types