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: Given a sid, collect all overwrites that should be applied for this
  sid at each quarter boundary.
  
  Parameters
  ----------
  group : pd.DataFrame
      The data for `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_idx : int
      The sid's index in the asset index.
  columns : list of BoundColumn
      The columns for which the overwrites should be computed.
  all_adjustments_for_sid : 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 for the given sid (`sid`). This dictionary is modified as
      adjustments are collected.
  sid : int
      The sid for which overwrites should be computed.
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code: def collect_overwrites_for_sid(
      self,
      group,
      dates,
      requested_qtr_data,
      last_per_qtr,
      sid_idx,
      columns,
      all_adjustments_for_sid,
      sid,
  ):
      """
      Given a sid, collect all overwrites that should be applied for this
      sid at each quarter boundary.
  
      Parameters
      ----------
      group : pd.DataFrame
          The data for `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_idx : int
          The sid's index in the asset index.
      columns : list of BoundColumn
          The columns for which the overwrites should be computed.
      all_adjustments_for_sid : 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 for the given sid (`sid`). This dictionary is modified as
          adjustments are collected.
      sid : int
          The sid for which overwrites should be computed.
      """
      # If data was requested for only 1 date, there can never be any
      # overwrites, so skip the extra work.
      if len(dates) == 1:
          return
  
      next_qtr_start_indices = dates.searchsorted(
          group[EVENT_DATE_FIELD_NAME].values,
          side=self.searchsorted_side,
      )
  
      qtrs_with_estimates = group.index.get_level_values(
          NORMALIZED_QUARTERS
      ).values
      for idx in next_qtr_start_indices:
          if 0 < idx < len(dates):
              # Find the quarter being requested in the quarter we're
              # crossing into.
              requested_quarter = requested_qtr_data[
                  SHIFTED_NORMALIZED_QTRS,
                  sid,
              ].iloc[idx]
              # Only add adjustments if the next quarter starts somewhere
              # in our date index for this sid. Our 'next' quarter can
              # never start at index 0; a starting index of 0 means that
              # the next quarter's event date was NaT.
              self.create_overwrites_for_quarter(
                  all_adjustments_for_sid,
                  idx,
                  last_per_qtr,
                  qtrs_with_estimates,
                  requested_quarter,
                  sid,
                  sid_idx,
                  columns,
              )
  
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 8 for calling the EarningsEstimatesLoader obj's underlying member method code with example usage: obj.collect_overwrites_for_sid(group, dates, requested_qtr_data, last_per_qtr, sid_idx, columns, all_adjustments_for_sid, sid) without return types