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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def initialize_model(self, model):
 """ Initializes internal state and build fp32 master copy of weights. :param model: fp16 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