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.
docstr: Re-initialize each random stream.
  
  Parameters
  ----------
  seed : None or integer in range 0 to 2**30
      Each random stream will be assigned a unique state that depends
      deterministically on this value.
  
  Returns
  -------
  None
docstr_tok: ["Re-initialize","each","random","stream",".","Parameters","--","--","--","--","--","seed",":","None","or","integer","in","range","0","to","2","*","*","30","Each","random","stream","will","be","assigned","a","unique","state","that","depends","deterministically","on","this","value",".","Returns","--","--","--","-","None"]
code: def seed(self, seed=None):
      """
      Re-initialize each random stream.
  
      Parameters
      ----------
      seed : None or integer in range 0 to 2**30
          Each random stream will be assigned a unique state that depends
          deterministically on this value.
  
      Returns
      -------
      None
  
      """
      if seed is None:
          seed = self.default_instance_seed
      self.set_rstate(seed)
  
      for old_r, new_r, size, nstreams in self.state_updates:
          if nstreams is None:
              nstreams = self.n_streams(size)
          rstates = self.get_substream_rstates(
              nstreams, new_r.owner.outputs[1].dtype
          )
          assert (
              old_r.get_value(
                  borrow=True, return_internal_type=True
              ).shape
              == rstates.shape
          )
          assert rstates.dtype == old_r.dtype
          old_r.set_value(rstates, borrow=True)
  
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 4 for calling the MRG_RandomStreams obj's underlying member method code with example usage: obj.seed(seed) without return types