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","."]
code: def __init__(
      self,
      estimates,
      name_map,
      split_adjustments_loader,
      split_adjusted_column_names,
      split_adjusted_asof,
  ):
      validate_split_adjusted_column_specs(
          name_map, split_adjusted_column_names
      )
      self._split_adjustments = split_adjustments_loader
      self._split_adjusted_column_names = split_adjusted_column_names
      self._split_adjusted_asof = split_adjusted_asof
      self._split_adjustment_dict = {}
      super(SplitAdjustedEstimatesLoader, self).__init__(
          estimates, name_map
      )
  
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 1 to create a new SplitAdjustedEstimatesLoader object from inherited base classes: EarningsEstimatesLoader with example: obj = SplitAdjustedEstimatesLoader(estimates, name_map, split_adjustments_loader, split_adjusted_column_names, split_adjusted_asof)