class_docstr: A PrecomputedLoader that emits arrays randomly-generated with a given seed.
  
  Parameters
  ----------
  seed : int
      Seed for numpy.random.RandomState.
  columns : list[BoundColumn]
      Columns that this loader should know about.
  dates : iterable[datetime-like]
      Same as PrecomputedLoader.
  sids : iterable[int-like]
      Same as PrecomputedLoader
docstr: Return uniformly-distributed dates in 2014.
code: def _datetime_values(self, shape):
      """
      Return uniformly-distributed dates in 2014.
      """
      start = Timestamp("2014", tz="UTC").asm8
      offsets = self.state.randint(
          low=0,
          high=364,
          size=shape,
      ).astype("timedelta64[D]")
      return start + offsets
  
imports: from interface import implements
  from numpy import arange, array, eye, float64, full, iinfo, nan, 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 zipline.data.adjustments import SQLiteAdjustmentReader, SQLiteAdjustmentWriter
  from zipline.data.bcolz_daily_bars import US_EQUITY_PRICING_BCOLZ_COLUMNS
  from zipline.utils.numpy_utils import bool_dtype, datetime64ns_dtype, float64_dtype, int64_dtype, object_dtype
label_desc: Use image node_id 6 for calling the SeededRandomLoader obj's underlying member method code with example usage: obj._datetime_values(shape) and returns: unknown