skipna=False`` is specified and
        missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
        is used as for logical operations.

        Parameters
        ----------
        skipna : bool, default True
            Exclude NA values. If the entire array is NA and `skipna` is
            True, then the result will be False, as for an empty array.
            If `skipna` is False, the result will still be True if there is
            at least one element that is truthy, otherwise NA will be returned
            if there are NA's present.

        Returns
        -------
        bool or :attr:`pandas.NA`

        See Also
        --------
        ArrowExtensionArray.all : Return whether all elements are truthy.

        Examples
        --------
        The result indicates whether any element is truthy (and by default
        skips NAs):

        >>> pd.array([True, False, True], dtype="boolean[pyarrow]").any()
        True
        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").any()
        True
        >>> pd.array([False, False, pd.NA], dtype="boolean[pyarrow]").any()
        False
        >>> pd.array([], dtype="boolean[pyarrow]").any()
        False
        >>> pd.array([pd.NA], dtype="boolean[pyarrow]").any()
        False
        >>> pd.array([pd.NA], dtype="float64[pyarrow]").any()
        False

        With ``skipna=False``, the result can be NA if this is logically
        required (whether ``pd.NA`` is True or False influences the result):

        >>> pd.array([True, False, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
        True
        >>> pd.array([1, 0, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
        True
        >>> pd.array([False, False, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
        <NA>
        >>> pd.array([0, 0, pd.NA], dtype="boolean[pyarrow]").any(skipna=False)
        <NA>
        r,