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 integers between 0 and 100.
docstr_tok: ["Return","uniformly-distributed","integers","between","0","and","100","."]
code: def _int_values(self, shape):
      """
      Return uniformly-distributed integers between 0 and 100.
      """
      return self.state.randint(low=0, high=100, size=shape).astype(
          "int64"
      )
  
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 6 for calling the SeededRandomLoader obj's underlying member method code with example usage: obj._int_values(shape) and returns: self