e name associated with it. State variables are accessible as
        attributes of the metric i.e, if ``name`` is ``"my_state"`` then its value can be accessed from an instance
        ``metric`` as ``metric.my_state``. Metric states behave like buffers and parameters of :class:`~torch.nn.Module`
        as they are also updated when ``.to()`` is called. Unlike parameters and buffers, metric states are not by
        default saved in the modules :attr:`~torch.nn.Module.state_dict`.

        Args:
            name: The name of the state variable. The variable will then be accessible at ``self.name``.
            default: Default value of the state; can either be a :class:`~torch.Tensor` or an empty list.
                The state will be reset to this value when ``self.reset()`` is called.
            dist_reduce_fx (Optional): Function to reduce state across multiple processes in distributed mode.
                If value is ``"sum"``, ``"mean"``, ``"cat"``, ``"min"`` or ``"max"`` we will use ``torch.sum``,
                ``torch.mean``, ``torch.cat``, ``torch.min`` and ``torch.max``` respectively, each with argument
                ``dim=0``. Note that the ``"cat"`` reduction only makes sense if the state is a list, and not
                a tensor. The user can also pass a custom function in this parameter.
            persistent (Optional): whether the state will be saved as part of the modules ``state_dict``.
                Default is ``False``.

        Note:
            Setting ``dist_reduce_fx`` to None will return the metric state synchronized across different processes.
            However, there won't be any reduction function applied to the synchronized metric state.

            The metric states would be synced as follows

            - If the metric state is :class:`~torch.Tensor`, the synced value will be a stacked :class:`~torch.Tensor`
              across the process dimension if the metric state was a :class:`~torch.Tensor`. The original
              :class:`~torch.Tensor` metric state retains dimension and hence the synchronized output will be of shape
              ``(num_process, ...)``.

            - If the metric state is a ``list``, the synced value will be a ``list`` containing the
              combined elements from all processes.

        Note:
            When passing a custom function to ``dist_reduce_fx``, expect the synchronized metric state to follow
            the format discussed in the above note.

        Raises:
            ValueError:
                If ``default`` is not a ``tensor`` or an ``empty list``.
            ValueError:
                If ``dist_reduce_fx`` is not callable or one of ``"mean"``, ``"sum"``, ``"cat"``, ``"min"``,
                ``"max"`` or ``None``.

        zPstate variable must be a tensor or any empty list (where you can append tensors)Ú