class_docstr: Synthetic PipelineLoader that uses a pre-computed array for each column.
  
  Parameters
  ----------
  values : dict
      Map from column to values to use for that column.
      Values can be anything that can be passed as the first positional
      argument to a DataFrame whose indices are ``dates`` and ``sids``
  dates : iterable[datetime-like]
      Row labels for input data.  Can be anything that pd.DataFrame will
      coerce to a DatetimeIndex.
  sids : iterable[int-like]
      Column labels for input data.  Can be anything that pd.DataFrame will
      coerce to an Int64Index.
  
  Notes
  -----
  Adjustments are unsupported by this loader.
docstr: Load by delegating to sub-loaders.
docstr_tok: ["Load","by","delegating","to","sub-loaders","."]
code: def load_adjusted_array(self, columns, dates, assets, mask):
      """
      Load by delegating to sub-loaders.
      """
      out = {}
      for col in columns:
          try:
              loader = self._loaders[col]
          except KeyError:
              raise ValueError("Couldn't find loader for %s" % col)
          out.update(
              loader.load_adjusted_array([col], dates, assets, mask)
          )
      return out
  
imports: from numpy import arange, array, eye, float64, full, iinfo, uint32
  from numpy.random import RandomState
  from pandas import DataFrame, Timestamp
  from six import iteritems
  from sqlite3 import connect
  from .base import PipelineLoader
  from .frame import DataFrameLoader
  from catalyst.data.us_equity_pricing import SQLiteAdjustmentReader, SQLiteAdjustmentWriter, US_EQUITY_PRICING_BCOLZ_COLUMNS
  from catalyst.utils.numpy_utils import bool_dtype, datetime64ns_dtype, float64_dtype, int64_dtype, object_dtype
label_desc: Use image node_id 2 for calling the PrecomputedLoader obj's underlying member method code with example usage: obj.load_adjusted_array(columns, dates, assets, mask) and returns: out