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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 load_adjusted_array(self, domain, columns, dates, sids, mask):
 """ Load data from our stored baseline. """

        if len(columns) != 1:
            raise ValueError(
                "Can't load multiple columns with DataFrameLoader"
            )

        column = columns[0]
        self._validate_input_column(column)

        date_indexer = self.dates.get_indexer(dates)
        assets_indexer = self.assets.get_indexer(sids)

        # Boolean arrays with True on matched entries
        good_dates = (date_indexer != -1)
        good_assets = (assets_indexer != -1)

        data = self.baseline[ix_(date_indexer, assets_indexer)]
        mask = (good_assets & as_column(good_dates)) & mask

        # Mask out requested columns/rows that didn't match.
        data[~mask] = column.missing_value

        return {
            column: AdjustedArray(
                # Pull out requested columns/rows from our baseline data.
                data=data,
                adjustments=self.format_adjustments(dates, sids),
                missing_value=column.missing_value,
            ),
        }