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 pretty_return(
      self, node_rstate, new_rstate, sample, size, nstreams
  ):
      # TODO : need description for method, parameter and return
      sample.rstate = node_rstate
      sample.update = (node_rstate, new_rstate)
      self.state_updates.append(
          (node_rstate, new_rstate, size, nstreams)
      )
      node_rstate.default_update = new_rstate
      return sample
  
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 8 for calling the MRG_RandomStreams obj's underlying member method code with example usage: obj.pretty_return(node_rstate, new_rstate, sample, size, nstreams) and returns: sample