class_docstr: Module component with similar interface to numpy.random
  (numpy.random.RandomState)
  
  Parameters
  ----------
  seed: None or int
      A default seed to initialize the RandomState
      instances after build.  See `RandomStreamsInstance.__init__`
      for more details.
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
  
      seedgen = np.random.RandomState(seed)
      for old_r, new_r in self.state_updates:
          old_r_seed = seedgen.randint(2**30)
          old_r.set_value(
              np.random.RandomState(int(old_r_seed)), borrow=True
          )
  
file_path: Theano/theano/tensor/shared_randomstreams.py
imports: from __future__ import absolute_import, print_function, division
  import copy
  import numpy
  from theano.compile.sharedvalue import SharedVariable, shared_constructor, shared
  from theano.tensor import raw_random
label_desc: Use image node_id 3 for calling the RandomStreams obj's underlying member method code with example usage: obj.seed(seed) without return types