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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 check_aggregate(self, variable, components=None, exclude_on_fail=False, multiplier=1, **kwargs):
 """Check whether a timeseries matches the aggregation of its components Parameters variable: str variable to be checked for matching aggregation of sub-categories components: list of str, default None list of variables, defaults to all sub-categories of `variable` exclude_on_fail: boolean, default False flag scenarios failing validation as `exclude: True` multiplier: number, default 1 factor when comparing variable and sum of components kwargs: passed to `np.isclose()` """

        # compute aggregate from components, return None if no components
        df_components = self.aggregate(variable, components)
        if df_components is None:
            return

        # filter and groupby data, use `pd.Series.align` for matching index
        rows = self._apply_filters(variable=variable)
        df_variable, df_components = (
            _aggregate(self.data[rows], 'variable').align(df_components)
        )

        # use `np.isclose` for checking match
        diff = df_variable[~np.isclose(df_variable, multiplier * df_components,
                                       **kwargs)]

        if len(diff):
            msg = '`{}` - {} of {} rows are not aggregates of components'
            logger().info(msg.format(variable, len(diff), len(df_variable)))

            if exclude_on_fail:
                self._exclude_on_fail(diff.index.droplevel([2, 3, 4]))

            return IamDataFrame(diff, variable=variable).timeseries()