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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 cov(self, other=None, pairwise=None, bias=False, **kwargs):
 """ Exponential weighted sample covariance. """

        if other is None:
            other = self._selected_obj
            # only default unset
            pairwise = True if pairwise is None else pairwise
        other = self._shallow_copy(other)

        def _get_cov(X, Y):
            X = self._shallow_copy(X)
            Y = self._shallow_copy(Y)
            cov = libwindow.ewmcov(X._prep_values(), Y._prep_values(),
                                   self.com, int(self.adjust),
                                   int(self.ignore_na), int(self.min_periods),
                                   int(bias))
            return X._wrap_result(cov)

        return _flex_binary_moment(self._selected_obj, other._selected_obj,
                                   _get_cov, pairwise=bool(pairwise))