content: import torch
  from torch import nn
  
  
  class LitEma(nn.Module):
      def __init__(self, model, decay=0.9999, use_num_updates=True):
          super().__init__()
          if decay < 0.0 or decay > 1.0:
              raise ValueError('Decay must be between 0 and 1')
  
          self.m_name2s_name = {}
          self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
          self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_updates
          else torch.tensor(-1, dtype=torch.int))
  
          for name, p in model.named_parameters():
              if p.requires_grad:
                  # remove as '.'-character is not allowed in buffers
                  s_name = name.replace('.', '_____')
                  self.m_name2s_name.update({name: s_name})
                  self.register_buffer(s_name, p.clone().detach().data)
  
          self.collected_params = []
  
      def forward(self, model):
          decay = self.decay
  
          if self.num_updates >= 0:
              self.num_updates += 1
              decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
  
          one_minus_decay = 1.0 - decay
  
          with torch.no_grad():
              m_param = dict(model.named_parameters())
              shadow_params = dict(self.named_buffers())
  
              for key in m_param:
                  if m_param[key].requires_grad:
                      sname = self.m_name2s_name[key]
                      shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                      shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
                  else:
                      assert not key in self.m_name2s_name
  
      def copy_to(self, model):
          m_param = dict(model.named_parameters())
          shadow_params = dict(self.named_buffers())
          for key in m_param:
              if m_param[key].requires_grad:
                  m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
              else:
                  assert not key in self.m_name2s_name
  
      def store(self, model):
          """
          Save the current parameters for restoring later.
          Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
              temporarily stored.
          """
          self.collected_params = [param.clone() for param in model.parameters()]
  
      def restore(self, model):
          """
          Restore the parameters stored with the `store` method.
          Useful to validate the model with EMA parameters without affecting the
          original optimization process. Store the parameters before the
          `copy_to` method. After validation (or model saving), use this to
          restore the former parameters.
          Args:
            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
              updated with the stored parameters.
          """
          for c_param, param in zip(self.collected_params, model.parameters()):
              param.data.copy_(c_param.data)
  