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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 where(self, cond, other, **kwargs):
 """Gets values from this manager where cond is true else from other. Args: cond: Condition on which to evaluate values. Returns: New DataManager with updated data and index. """


        assert isinstance(
            cond, type(self)
        ), "Must have the same DataManager subclass to perform this operation"
        if isinstance(other, type(self)):
            # Note: Currently we are doing this with two maps across the entire
            # data. This can be done with a single map, but it will take a
            # modification in the `BlockPartition` class.
            # If this were in one pass it would be ~2x faster.
            # TODO (devin-petersohn) rewrite this to take one pass.
            def where_builder_first_pass(cond, other, **kwargs):
                return cond.where(cond, other, **kwargs)

            def where_builder_second_pass(df, new_other, **kwargs):
                return df.where(new_other.eq(True), new_other, **kwargs)

            first_pass = cond._inter_manager_operations(
                other, "left", where_builder_first_pass
            )
            final_pass = self._inter_manager_operations(
                first_pass, "left", where_builder_second_pass
            )
            return self.__constructor__(final_pass.data, self.index, self.columns)
        else:
            axis = kwargs.get("axis", 0)
            # Rather than serializing and passing in the index/columns, we will
            # just change this index to match the internal index.
            if isinstance(other, pandas.Series):
                other.index = pandas.RangeIndex(len(other.index))

            def where_builder_series(df, cond):
                if axis == 0:
                    df.index = pandas.RangeIndex(len(df.index))
                    cond.index = pandas.RangeIndex(len(cond.index))
                else:
                    df.columns = pandas.RangeIndex(len(df.columns))
                    cond.columns = pandas.RangeIndex(len(cond.columns))
                return df.where(cond, other, **kwargs)

            reindexed_self, reindexed_cond, a = self.copartition(
                axis, cond, "left", False
            )
            # Unwrap from list given by `copartition`
            reindexed_cond = reindexed_cond[0]
            new_data = reindexed_self.inter_data_operation(
                axis, lambda l, r: where_builder_series(l, r), reindexed_cond
            )
            return self.__constructor__(new_data, self.index, self.columns)