class_docstr: Estimates loader that loads data that needs to be split-adjusted.
  
  Parameters
  ----------
  split_adjustments_loader : SQLiteAdjustmentReader
      The loader to use for reading split adjustments.
  split_adjusted_column_names : iterable of str
      The column names that should be split-adjusted.
  split_adjusted_asof : pd.Timestamp
      The date that separates data into 2 halves: the first half is the set
      of dates up to and including the split_adjusted_asof date. All
      adjustments occurring during this first half are applied  to all
      dates in this first half. The second half is the set of dates after
      the split_adjusted_asof date. All adjustments occurring during this
      second half are applied sequentially as they appear in the timeline.
class_docstr_tok: ["Estimates","loader","that","loads","data","that","needs","to","be","split-adjusted",".","Parameters","--","--","--","--","--","split_adjustments_loader",":","SQLiteAdjustmentReader","The","loader","to","use","for","reading","split","adjustments",".","split_adjusted_column_names",":","iterable","of","str","The","column","names","that","should","be","split-adjusted",".","split_adjusted_asof",":","pd.Timestamp","The","date","that","separates","data","into","2","halves",":","the","first","half","is","the","set","of","dates","up","to","and","including","the","split_adjusted_asof","date",".","All","adjustments","occurring","during","this","first","half","are","applied","to","all","dates","in","this","first","half",".","The","second","half","is","the","set","of","dates","after","the","split_adjusted_asof","date",".","All","adjustments","occurring","during","this","second","half","are","applied","sequentially","as","they","appear","in","the","timeline","."]
docstr: Calculates both split adjustments and overwrites for all sids.
docstr_tok: ["Calculates","both","split","adjustments","and","overwrites","for","all","sids","."]
code: def get_adjustments(
      self,
      zero_qtr_data,
      requested_qtr_data,
      last_per_qtr,
      dates,
      assets,
      columns,
      **kwargs,
  ):
      """
      Calculates both split adjustments and overwrites for all sids.
      """
      split_adjusted_cols_for_group = [
          self.name_map[col.name]
          for col in columns
          if self.name_map[col.name]
          in self._split_adjusted_column_names
      ]
      # Add all splits to the adjustment dict for this sid.
      split_adjusted_asof_idx = self.get_split_adjusted_asof_idx(dates)
      return super(SplitAdjustedEstimatesLoader, self).get_adjustments(
          zero_qtr_data,
          requested_qtr_data,
          last_per_qtr,
          dates,
          assets,
          columns,
          split_adjusted_cols_for_group=split_adjusted_cols_for_group,
          split_adjusted_asof_idx=split_adjusted_asof_idx,
      )
  
imports: from abc import abstractmethod, abstractproperty
  from interface import implements
  import numpy
  import pandas
  from six import viewvalues
  from toolz import groupby
  from zipline.lib.adjusted_array import AdjustedArray
  from zipline.lib.adjustment import Datetime641DArrayOverwrite, Datetime64Overwrite, Float641DArrayOverwrite, Float64Multiply, Float64Overwrite
  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.utils.numpy_utils import datetime64ns_dtype, float64_dtype
  from zipline.pipeline.loaders.utils import ffill_across_cols, last_in_date_group
label_desc: Use image node_id 4 for calling the SplitAdjustedEstimatesLoader obj's underlying member method code with example usage: obj.get_adjustments(zero_qtr_data, requested_qtr_data, last_per_qtr, dates, assets, columns) and returns: super