code: def __call__(self, module, step=None):
      if step is None:
          mu = self.mu
      else:
          mu = min(self.mu, (1.0 + step) / (10 + step))
  
      def strip_module(s: str) -> str:
          return s
  
      mesd = self.module_ema.state_dict()
      with torch.no_grad():
          for name, x in module.state_dict().items():
              if name.endswith("num_batches_tracked"):
                  continue
              n = strip_module(name)
              mesd[n].mul_(mu)
              mesd[n].add_((1.0 - mu) * x)
  
imports: import copy
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import Optional
  import torch
  import warnings
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the EMA obj's underlying member method code with example usage: obj.__call__(module, step) and returns: s