class_docstr: Module component with similar interface to `numpy.random.Generator`.
  
  Attributes
  ----------
  seed: None or int
      A default seed to initialize the `Generator` instances after build.
  state_updates: list
      A list of pairs of the form ``(input_r, output_r)``.  This will be
      over-ridden by the module instance to contain stream generators.
  default_instance_seed: int
      Instance variable should take None or integer value. Used to seed the
      random number generator that provides seeds for member streams.
  gen_seedgen: numpy.random.Generator
      `Generator` instance that `RandomStream.gen` uses to seed new
      streams.
  rng_ctor: type
      Constructor used to create the underlying RNG objects.  The default
      is `np.random.default_rng`.
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code: def updates(self):
      return list(self.state_updates)
  
imports: from collections.abc import Sequence
  from functools import wraps
  from itertools import zip_longest
  from types import ModuleType
  from typing import TYPE_CHECKING, Literal, Optional, Union
  import numpy
  from pytensor.compile.sharedvalue import shared
  from pytensor.graph.basic import Constant, Variable
  from pytensor.scalar import ScalarVariable
  from pytensor.tensor import get_vector_length
  from pytensor.tensor.basic import as_tensor_variable, cast, constant
  from pytensor.tensor.extra_ops import broadcast_to
  from pytensor.tensor.math import maximum
  from pytensor.tensor.shape import specify_shape
  from pytensor.tensor.type import int_dtypes
  from pytensor.tensor.variable import TensorVariable
label_desc: Use image node_id 3 for calling the RandomStream obj's underlying member method code with example usage: obj.updates() and returns: list