class_docstr: A Type wrapper for `numpy.random.RandomState`.
  
  The reason this exists (and `Generic` doesn't suffice) is that
  `RandomState` objects that would appear to be equal do not compare equal
  with the ``==`` operator.
  
  This `Type` also works with a ``dict`` derived from
  `RandomState.get_state(legacy=False)`, unless the ``strict`` argument to `Type.filter`
  is explicitly set to ``True``.
class_docstr_tok: ["A","Type","wrapper","for","`","numpy.random.RandomState","`",".","The","reason","this","exists","(","and","`","Generic","`","does","n't","suffice",")","is","that","`","RandomState","`","objects","that","would","appear","to","be","equal","do","not","compare","equal","with","the","``","==","``","operator",".","This","`","Type","`","also","works","with","a","``","dict","``","derived","from","`","RandomState.get_state","(","legacy=False",")","`",",","unless","the","``","strict","``","argument","to","`","Type.filter","`","is","explicitly","set","to","``","True","``","."]
code: def __hash__(self):
      return hash(type(self))
  
imports: from typing import TypeVar
  import numpy
  import pytensor
  from pytensor.graph.type import Type
label_desc: Use image node_id 5 for calling the RandomStateType obj's underlying member method code with example usage: obj.__hash__() and returns: hash