class_docstr: Return a sparse having random values from a Poisson density
  with mean from the input.
  
  WARNING: This Op is NOT deterministic, as calling it twice with the
  same inputs will NOT give the same result. This is a violation of
  Theano's contract for Ops
  
  :param x: Sparse matrix.
  
  :return: A sparse matrix of random integers of a Poisson density
           with mean of `x` element wise.
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code: def make_node(self, x):
      x = as_sparse_variable(x)
      return gof.Apply(self, [x], [x.type()])
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  from six.moves import xrange
  import theano
  import scipy.sparse
  from theano import gof, tensor
  from theano.tensor import discrete_dtypes, float_dtypes
  from theano.tensor.opt import register_specialize
  from theano.sparse.basic import as_sparse_variable, SparseType, add_s_s, neg, mul_s_s, mul_s_d, dot, CSMProperties, CSM, _is_sparse_variable, _is_dense_variable, CSC, CSR, csm_properties, csm_data, csm_indices, csm_indptr, csm_shape, _is_sparse, Remove0, remove0, Cast, bcast, wcast, icast, lcast, fcast, dcast, ccast, zcast, HStack, hstack, VStack, vstack, AddSSData, add_s_s_data, MulSV, mul_s_v, structured_monoid, structured_sigmoid, structured_exp, structured_log, structured_pow, structured_minimum, structured_maximum, structured_add, StructuredAddSV, structured_add_s_v, SamplingDot, sampling_dot
  from theano.sparse.opt import MulSDCSC, mul_s_d_csc, MulSDCSR, mul_s_d_csr, MulSVCSR, mul_s_v_csr, StructuredAddSVCSR, structured_add_s_v_csr, SamplingDotCSR, sampling_dot_csr, local_mul_s_d, local_mul_s_v, local_structured_add_s_v, local_sampling_dot_csr
label_desc: Use image node_id 1 for calling the Poisson obj's underlying member method code with example usage: obj.make_node(x) and returns: gof