docstr: Sample from a normal distribution centered on avg with
  the specified standard deviation (std).
  
  If the size argument is ambiguous on the number of dimensions,
  ndim may be a plain integer to supplement the missing information.
docstr_tok: ["Sample","from","a","normal","distribution","centered","on","avg","with","the","specified","standard","deviation","(","std",")",".","If","the","size","argument","is","ambiguous","on","the","number","of","dimensions",",","ndim","may","be","a","plain","integer","to","supplement","the","missing","information","."]
code: def normal(self, size=None, avg=0.0, std=1.0, ndim=None, dtype=None):
      """
      Sample from a normal distribution centered on avg with
      the specified standard deviation (std).
  
      If the size argument is ambiguous on the number of dimensions,
      ndim may be a plain integer to supplement the missing information.
  
      """
      return self.gen(normal, size, avg, std, ndim=ndim, dtype=dtype)
  
imports: from __future__ import absolute_import, print_function, division
  import sys
  from copy import copy
  import numpy
  from six import string_types
  from six.moves import reduce, xrange
  import theano
  from theano import tensor
  from theano.tensor import opt
  from theano import gof
  from theano.compile import optdb
label_desc: Use image node_id 3 for calling the RandomStreamsBase obj's underlying member method code with example usage: obj.normal(size, avg, std, ndim, dtype) and returns: self