code: def _update_ema(self):
      for rate, params in zip(self.ema_rate, self.ema_params):
          update_ema(params, self.mp_trainer.master_params, rate=rate)
  
imports: import copy
  import functools
  import os
  import blobfile
  import torch
  import torch.distributed
  from torch.nn.parallel.distributed import DistributedDataParallel
  from torch.optim import RAdam
  from .None import dist_util, logger
  from .fp16_util import MixedPrecisionTrainer
  from .nn import update_ema
  from .resample import LossAwareSampler, UniformSampler
  from .fp16_util import get_param_groups_and_shapes, make_master_params, master_params_to_model_params
  import numpy
label_desc: Use image node_id 8 for calling the TrainLoop obj's underlying member method code with example usage: obj._update_ema() without return types