def register_buffer(
        self, name: str, tensor: Optional[Tensor], persistent: bool = True
    ) -> None:
        r"""Adds a buffer to the module.

        This is typically used to register a buffer that should not to be
        considered a model parameter. For example, BatchNorm's ``running_mean``
        is not a parameter, but is part of the module's state. Buffers, by
        default, are persistent and will be saved alongside parameters. This
        behavior can be changed by setting :attr:`persistent` to ``False``. The
        only difference between a persistent buffer and a non-persistent buffer
        is that the latter will not be a part of this module's
        :attr:`state_dict`.

        Buffers can be accessed as attributes using given names.

        Args:
            name (str): name of the buffer. The buffer can be accessed
                from this module using the given name
            tensor (Tensor or None): buffer to be registered. If ``None``, then operations
                that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,
                the buffer is **not** included in the module's :attr:`state_dict`.
            persistent (bool): whether the buffer is part of this module's
                :attr:`state_dict`.

        Example::

            >>> self.register_buffer('running_mean', zeros(num_features))

        """
        if "_buffers" not in self.__dict__:
            raise AttributeError("cannot assign buffer before Module.__init__() call")
        elif "." in name:
            raise KeyError('buffer name can\'t contain "."')
        elif name == "":
            raise KeyError('buffer name can\'t be empty string ""')
        elif hasattr(self, name) and name not in self._buffers:
            raise KeyError("attribute '{}' already exists".format(name))
        elif tensor is not None and not isinstance(tensor, Tensor):
            raise TypeError(
                "cannot assign '{}' object to buffer '{}' "
                "(torch Tensor or None required)".format(typename(tensor), name)
            )
        else:
            self._buffers[name] = tensor
            if persistent:
                self._non_persistent_buffers_set.discard(name)
            else:
                self._non_persistent_buffers_set.add(name)
Adds a buffer to the module.

        This is typically used to register a buffer that should not to be
        considered a model parameter. For example, BatchNorm's ``running_mean``
        is not a parameter, but is part of the module's state. Buffers, by
        default, are persistent and will be saved alongside parameters. This
        behavior can be changed by setting :attr:`persistent` to ``False``. The
        only difference between a persistent buffer and a non-persistent buffer
        is that the latter will not be a part of this module's
        :attr:`state_dict`.

        Buffers can be accessed as attributes using given names.

        Args:
            name (str): name of the buffer. The buffer can be accessed
                from this module using the given name
            tensor (Tensor or None): buffer to be registered. If ``None``, then operations
                that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,
                the buffer is **not** included in the module's :attr:`state_dict`.
            persistent (bool): whether the buffer is part of this module's
                :attr:`state_dict`.

        Example::

            >>> self.register_buffer('running_mean', zeros(num_features))

        
python/aitemplate/frontend/nn/module.py