class_docstr: An abstract pipeline loader for estimates data that can load data a
  variable number of quarters forwards/backwards from calendar dates
  depending on the `num_announcements` attribute of the columns' dataset.
  If split adjustments are to be applied, a loader, split-adjusted columns,
  and the split-adjusted asof-date must be supplied.
  
  Parameters
  ----------
  estimates : pd.DataFrame
      The raw estimates data.
      ``estimates`` must contain at least 5 columns:
          sid : int64
              The asset id associated with each estimate.
  
          event_date : datetime64[ns]
              The date on which the event that the estimate is for will/has
              occurred..
  
          timestamp : datetime64[ns]
              The datetime where we learned about the estimate.
  
          fiscal_quarter : int64
              The quarter during which the event has/will occur.
  
          fiscal_year : int64
              The year during which the event has/will occur.
  
  name_map : dict[str -> str]
      A map of names of BoundColumns that this loader will load to the
      names of the corresponding columns in `events`.
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docstr: Parameters
  ----------
  group : pd.DataFrame
      The data for the given sid.
  dates : pd.DatetimeIndex
      The calendar dates for which estimates data is requested.
  requested_qtr_data : pd.DataFrame
      The DataFrame with the latest values for the requested quarter
      for all columns.
  last_per_qtr : pd.DataFrame
      A DataFrame with a column MultiIndex of [self.estimates.columns,
      normalized_quarters, sid] that allows easily getting the timeline
      of estimates for a particular sid for a particular quarter.
  sid_to_idx : dict[int -> int]
      A dictionary mapping sid to he sid's index in the asset index.
  columns : list of BoundColumn
      The columns for which the overwrites should be computed.
  col_to_all_adjustments : dict[int -> AdjustedArray]
      A dictionary of the integer index of each timestamp into the date
      index, mapped to adjustments that should be applied at that
      index. This dictionary is for adjustments for ALL sids. It is
      modified as adjustments are collected.
  kwargs :
      Additional arguments used in collecting adjustments; unused here.
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code: def get_adjustments_for_sid(
      self,
      group,
      dates,
      requested_qtr_data,
      last_per_qtr,
      sid_to_idx,
      columns,
      col_to_all_adjustments,
      **kwargs,
  ):
      """
  
      Parameters
      ----------
      group : pd.DataFrame
          The data for the given sid.
      dates : pd.DatetimeIndex
          The calendar dates for which estimates data is requested.
      requested_qtr_data : pd.DataFrame
          The DataFrame with the latest values for the requested quarter
          for all columns.
      last_per_qtr : pd.DataFrame
          A DataFrame with a column MultiIndex of [self.estimates.columns,
          normalized_quarters, sid] that allows easily getting the timeline
          of estimates for a particular sid for a particular quarter.
      sid_to_idx : dict[int -> int]
          A dictionary mapping sid to he sid's index in the asset index.
      columns : list of BoundColumn
          The columns for which the overwrites should be computed.
      col_to_all_adjustments : dict[int -> AdjustedArray]
          A dictionary of the integer index of each timestamp into the date
          index, mapped to adjustments that should be applied at that
          index. This dictionary is for adjustments for ALL sids. It is
          modified as adjustments are collected.
      kwargs :
          Additional arguments used in collecting adjustments; unused here.
      """
      # Collect all adjustments for a given sid.
      all_adjustments_for_sid = {}
      sid = int(group.name)
      self.collect_overwrites_for_sid(
          group,
          dates,
          requested_qtr_data,
          last_per_qtr,
          sid_to_idx[sid],
          columns,
          all_adjustments_for_sid,
          sid,
      )
      self.merge_into_adjustments_for_all_sids(
          all_adjustments_for_sid, col_to_all_adjustments
      )
  
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 9 for calling the EarningsEstimatesLoader obj's underlying member method code with example usage: obj.get_adjustments_for_sid(group, dates, requested_qtr_data, last_per_qtr, sid_to_idx, columns, col_to_all_adjustments) without return types