class_docstr: Loader for sliding history windows, with support for adjustments.
  
  Parameters
  ----------
  trading_calendar: TradingCalendar
      Contains the grouping logic needed to assign minutes to periods.
  reader : DailyBarReader, MinuteBarReader
      Reader for pricing bars.
  adjustment_reader : SQLiteAdjustmentReader
      Reader for adjustment data.
class_docstr_tok: ["Loader","for","sliding","history","windows",",","with","support","for","adjustments",".","Parameters","--","--","--","--","--","trading_calendar",":","TradingCalendar","Contains","the","grouping","logic","needed","to","assign","minutes","to","periods",".","reader",":","DailyBarReader",",","MinuteBarReader","Reader","for","pricing","bars",".","adjustment_reader",":","SQLiteAdjustmentReader","Reader","for","adjustment","data","."]
code: def _array(self, start, end, assets, field):
      pass
  
imports: from abc import ABCMeta, abstractmethod, abstractproperty
  from numpy import concatenate
  from lru import LRU
  from pandas import isnull
  from toolz import sliding_window
  from six import with_metaclass
  from zipline.assets import Equity, Future
  from zipline.assets.continuous_futures import ContinuousFuture
  from zipline.lib._int64window import AdjustedArrayWindow
  from zipline.lib._float64window import AdjustedArrayWindow
  from zipline.lib.adjustment import Float64Multiply, Float64Add
  from zipline.utils.cache import ExpiringCache
  from zipline.utils.math_utils import number_of_decimal_places
  from zipline.utils.memoize import lazyval
  from zipline.utils.numpy_utils import float64_dtype
  from zipline.utils.pandas_utils import find_in_sorted_index, normalize_date
label_desc: Use image node_id 4 for calling the HistoryLoader obj's underlying member method code with example usage: obj._array(start, end, assets, field) without return types