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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 array(self) -> ExtensionArray: """ The ExtensionArray of the data backing this Series or Index. .. versionadded:: 0.24.0 Returns ------- ExtensionArray An ExtensionArray of the values stored within. For extension types, this is the actual array. For NumPy native types, this is a thin (no copy) wrapper around :class:`numpy.ndarray`. ``.array`` differs ``.values`` which may require converting the data to a different form. See Also -------- Index.to_numpy : Similar method that always returns a NumPy array. Series.to_numpy : Similar method that always returns a NumPy array. Notes ----- This table lays out the different array types for each extension dtype within pandas. ================== ============================= dtype array type ================== ============================= category Categorical period PeriodArray interval IntervalArray IntegerNA IntegerArray datetime64[ns, tz] DatetimeArray ================== ============================= For any 3rd-party extension types, the array type will be an ExtensionArray. For all remaining dtypes ``.array`` will be a :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray stored within. If you absolutely need a NumPy array (possibly with copying / coercing data), then use :meth:`Series.to_numpy` instead. Examples -------- For regular NumPy types like int, and float, a PandasArray is returned. <PandasArray> [1, 2, 3] Length: 3, dtype: int64 For extension types, like Categorical, the actual ExtensionArray is returned [a, b, a] Categories (2, object):
 [a, b] """

        result = self._values

        if is_datetime64_ns_dtype(result.dtype):
            from pandas.arrays import DatetimeArray
            result = DatetimeArray(result)
        elif is_timedelta64_ns_dtype(result.dtype):
            from pandas.arrays import TimedeltaArray
            result = TimedeltaArray(result)

        elif not is_extension_array_dtype(result.dtype):
            from pandas.core.arrays.numpy_ import PandasArray
            result = PandasArray(result)

        return result