def METHOD_NAME(self, wrapper: ModuleWrapper, target_name: str, target: Tensor):
    def amin_reduce_func(converted_target: Tensor):
        return converted_target.detach().amin(dim=-1)
    def amax_reduce_func(converted_target: Tensor):
        return converted_target.detach().amax(dim=-1)
    if target_name not in wrapper.quantization_target_spaces:
        return
    target_space = wrapper.quantization_target_spaces[target_name]
    # TODO sync the collection of data info when using ddp
    if target_space._scaler:
        current_amin = target_space._scaler.shrink(target, amin_reduce_func, keepdim=True)
        current_amax = target_space._scaler.shrink(target, amax_reduce_func, keepdim=True)
    else:
        current_amin = target.detach().reshape(-1).amin(-1)
        current_amax = target.detach().reshape(-1).amax(-1)
    # update
    target_space.tracked_max = update_tracked_value(target_space.tracked_max, current_amax, "max")
    target_space.tracked_min = update_tracked_value(target_space.tracked_min, current_amin, "min")