code: def load_adjusted_array(self, domain, columns, dates, sids, mask):
      # Only load requested columns.
      requested_column_names = [
          self._columns[column.name] for column in columns
      ]
  
      requested_spilt_adjusted_columns = [
          column_name
          for column_name in self._split_adjusted_column_names
          if column_name in requested_column_names
      ]
  
      raw = load_raw_data(
          sids,
          domain.data_query_cutoff_for_sessions(dates),
          self._expr[
              sorted(metadata_columns.union(requested_column_names))
          ],
          self._odo_kwargs,
          checkpoints=self._checkpoints,
      )
  
      return self.loader(
          raw,
          {
              column.name: self._columns[column.name]
              for column in columns
          },
          self._split_adjustments,
          requested_spilt_adjusted_columns,
          self._split_adjusted_asof,
      ).load_adjusted_array(
          domain,
          columns,
          dates,
          sids,
          mask,
      )
  
imports: from interface import implements
  from datashape import istabular
  from .core import bind_expression_to_resources
  from zipline.pipeline.common import EVENT_DATE_FIELD_NAME, FISCAL_QUARTER_FIELD_NAME, FISCAL_YEAR_FIELD_NAME, SID_FIELD_NAME, TS_FIELD_NAME
  from zipline.pipeline.loaders.base import PipelineLoader
  from zipline.pipeline.loaders.blaze.utils import load_raw_data
  from zipline.pipeline.loaders.earnings_estimates import NextEarningsEstimatesLoader, PreviousEarningsEstimatesLoader, required_estimates_fields, metadata_columns, PreviousSplitAdjustedEarningsEstimatesLoader, NextSplitAdjustedEarningsEstimatesLoader
label_desc: Use image node_id 2 for calling the BlazeSplitAdjustedEstimatesLoader obj's underlying member method code with example usage: obj.load_adjusted_array(domain, columns, dates, sids, mask) and returns: self