class_docstr: PipelineLoader for US Equity Pricing data
  
  Delegates loading of baselines and adjustments.
class_docstr_tok: ["PipelineLoader","for","US","Equity","Pricing","data","Delegates","loading","of","baselines","and","adjustments","."]
code: def load_adjusted_array(self, columns, dates, assets, mask):
      # load_adjusted_array is called with dates on which the user's algo
      # will be shown data, which means we need to return the data that would
      # be known at the start of each date.  We assume that the latest data
      # known on day N is the data from day (N - 1), so we shift all query
      # dates back by a day.
      start_date, end_date = _shift_dates(
          self._all_sessions,
          dates[0],
          dates[-1],
          shift=1,
      )
      colnames = [c.name for c in columns]
      raw_arrays = self.raw_price_loader.load_raw_arrays(
          colnames,
          start_date,
          end_date,
          assets,
      )
      adjustments = self.adjustments_loader.load_adjustments(
          colnames,
          dates,
          assets,
      )
  
      out = {}
      for c, c_raw, c_adjs in zip(columns, raw_arrays, adjustments):
          out[c] = AdjustedArray(
              c_raw.astype(c.dtype),
              mask,
              c_adjs,
              c.missing_value,
          )
      return out
  
file_path: catalyst/catalyst/pipeline/loaders/equity_pricing_loader.py
imports: from numpy import iinfo, uint32
  from catalyst.data.us_equity_pricing import BcolzDailyBarReader, SQLiteAdjustmentReader
  from catalyst.lib.adjusted_array import AdjustedArray
  from catalyst.errors import NoFurtherDataError
  from catalyst.utils.calendars import get_calendar
  from .base import PipelineLoader
label_desc: Use image node_id 3 for calling the USEquityPricingLoader obj's underlying member method code with example usage: obj.load_adjusted_array(columns, dates, assets, mask) and returns: out