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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 update_multi_precision(self, index, weight, grad, state):
 """Updates the given parameter using the corresponding gradient and state. Mixed precision version. Parameters index : int The unique index of the parameter into the individual learning rates and weight decays. Learning rates and weight decay may be set via `set_lr_mult()` and `set_wd_mult()`, respectively. weight : NDArray The parameter to be updated. grad : NDArray The gradient of the objective with respect to this parameter. state : any obj The state returned by `create_state()`. """

        if self.multi_precision and weight.dtype == numpy.float16:
            # Wrapper for mixed precision
            weight_master_copy = state[0]
            original_state = state[1]
            grad32 = grad.astype(numpy.float32)
            self.update(index, weight_master_copy, grad32, original_state)
            cast(weight_master_copy, dtype=weight.dtype, out=weight)
        else:
            self.update(index, weight, grad, state)