code: def initialize_model(self, model):
          
          logging.info('Initializing fp32 clone weights')
          self.fp16_model = model
          self.fp16_model.zero_grad()
          self.fp32_params = [param.to(torch.float32).detach()
                              for param in model.parameters()]
  
          for param in self.fp32_params:
              param.requires_grad = True
docs: Initializes internal state and build fp32 master copy of weights.
  
          :param model: fp16 model