docstr: Draw samples from a Poisson distribution.
  
  The Poisson distribution is the limit of the Binomial distribution for
  large N.
  
  If the size argument is ambiguous on the number of dimensions,
  ndim may be a plain integer to supplement the missing information.
docstr_tok: ["Draw","samples","from","a","Poisson","distribution",".","The","Poisson","distribution","is","the","limit","of","the","Binomial","distribution","for","large","N.","If","the","size","argument","is","ambiguous","on","the","number","of","dimensions",",","ndim","may","be","a","plain","integer","to","supplement","the","missing","information","."]
code: def poisson(self, size=None, lam=None, ndim=None, dtype="int64"):
      """
      Draw samples from a Poisson distribution.
  
      The Poisson distribution is the limit of the Binomial distribution for
      large N.
  
      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(poisson, size, lam, 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 6 for calling the RandomStreamsBase obj's underlying member method code with example usage: obj.poisson(size, lam, ndim, dtype) and returns: self