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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 get_parameter_or_create(name, shape=None, initializer=None, need_grad=True, as_need_grad=None):
 """ Returns an existing parameter variable with the provided name. If a variable with the provided name does not exist, a new variable with the provided name is returned. Args: name(str):
 The name under the current scope. If it already exists, the name is queried from the parameter manager. shape (:obj:`tuple` of :obj:`int`):
 Shape of created parameter. The shape of the specified parameter must match with this shape. The default is None which is only valid if initializer is given as an :obj:`numpy.ndarray`. initializer (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`):
 An initialization function to be applied to the parameter. :obj:`numpy.ndarray` can also be given to initialize parameters from numpy array data. need_grad (bool):
 Register the parameter with the specified ``need_grad`` flag. The default is True. If the flag is different from the previously specified one, the flag will be overwritten, but the values will be kept. as_need_grad (bool):
 Get a parameter variable with the specified ``need_grad`` flag. Note that this doesn't overwrite the flag of the registered parameter variable with the provided name. Instead, if the given flag mismatches with the previously registered ``need_grad`` flag, it returns a new variable referring to the same array contents but with ``need_grad=as_need_grad``. """

    names = name.split('/')
    if len(names) > 1:
        with parameter_scope(names[0]):
            return get_parameter_or_create('/'.join(names[1:]), shape, initializer, need_grad, as_need_grad)
    param = get_parameter(names[0])
    if param is None:
        class VariableInfo:
            pass
        info = VariableInfo()
        info.initializer = initializer

        if initializer is not None:
            if isinstance(initializer, numpy.ndarray):  # numpy init
                param = nn.Variable(initializer.shape, need_grad=need_grad)
                param.d = initializer
            # initializer init
            elif isinstance(initializer, nn.initializer.BaseInitializer) or initializer.__name__ == "<lambda>":
                assert shape is not None
                param = nn.Variable(shape, need_grad=need_grad)
                param.d = initializer(shape=param.shape)
            else:
                raise ValueError(
                    "`initializer` must be either the :obj:`numpy.ndarray` or an instance inherited from `nnabla.initializer.BaseInitializer`.")
        else:  # default init
            assert shape is not None
            param = nn.Variable(shape, need_grad=need_grad)
        set_parameter(name, param)
    else:
        if param.shape != tuple(shape):
            raise ValueError(
                'The size of existing parameter "{}" {} is different from the size of new parameter {}.\n'
                'To clear all parameters, call nn.clear_parameters().'.format(name, param.shape, tuple(shape)))
        if need_grad != param.need_grad:
            param.need_grad = need_grad
    if as_need_grad is None:
        return param
    if param.need_grad != as_need_grad:
        param = param.get_unlinked_variable(need_grad=as_need_grad)
    return param