class_docstr: Module component with similar interface to numpy.random
  (numpy.random.RandomState).
  
  Parameters
  ----------
  seed : int or list of 6 int
      A default seed to initialize the random state.
      If a single int is given, it will be replicated 6 times.
      The first 3 values of the seed must all be less than M1 = 2147483647,
      and not all 0; and the last 3 values must all be less than
      M2 = 2147462579, and not all 0.
code: def __init__(self, seed=12345):
      # A list of pairs of the form (input_r, output_r), representing the
      # update rules of all the random states generated
      # by this RandomStreams.
      self.state_updates = []
  
      super(MRG_RandomStreams, self).__init__()
  
      # Needed to reset the streams.
      self.default_instance_seed = seed
  
      self.set_rstate(seed)
  
imports: from __future__ import absolute_import, print_function, division
  import warnings
  import numpy
  from six import integer_types, string_types
  from six.moves import xrange
  import theano
  from theano import Op, Apply, shared, config, Variable
  from theano import gradient, function
  from theano.gradient import undefined_grad
  from theano import tensor
  from theano.tensor import TensorType, as_tensor_variable, get_vector_length, cast, opt, scal
  from theano.compile import optdb
  from theano.gof import local_optimizer, ParamsType
  from theano.scalar import bool, int32
  from .None import multinomial
label_desc: Use image node_id 2 to create a new MRG_RandomStreams object from inherited base classes: object with example: obj = MRG_RandomStreams(seed)