code: def test_gluon_poisson():
      class TestPoisson(HybridBlock):
          def __init__(self, func):
              self._func = func
              super(TestPoisson, self).__init__()
  
          def forward(self, rate, *args):
              poisson = mgp.Poisson(rate, validate_args=True)
              return _distribution_method_invoker(
                  poisson, self._func, *args
              )
  
      shapes = [(1,), (2, 3), 6]
      # Test sampling
      for shape, hybridize in itertools.product(shapes, [True, False]):
          rate = np.random.uniform(0.5, 1.5, shape)
          net = TestPoisson("sample")
          if hybridize:
              net.hybridize()
          mx_out = net(rate).asnumpy()
          assert mx_out.shape == rate.shape
  
      # Test log_prob
      for shape, hybridize in itertools.product(shapes, [True, False]):
          rate = np.random.uniform(0.5, 1.5, shape)
          samples = np.random.randint(0, 5, shape).astype("float")
          net = TestPoisson("log_prob")
          if hybridize:
              net.hybridize()
          mx_out = net(rate, samples).asnumpy()
          np_out = ss.poisson(mu=rate.asnumpy()).logpmf(
              samples.asnumpy()
          )
          assert_almost_equal(
              mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False
          )
  
imports: import mxnet
  import numpy
  from mxnet import np, npx, autograd
  from mxnet import gluon
  import mxnet.gluon.probability
  from mxnet.gluon.probability import StochasticBlock, StochasticSequential
  from mxnet.gluon import HybridBlock
  from mxnet.test_utils import use_np, assert_almost_equal
  from numpy.testing import assert_array_equal
  import pytest
  import scipy.stats
  import scipy.special
  import itertools
  from numbers import Number
label_desc: Use image node_id 8 for calling a global function with example usage: test_gluon_poisson() and returns: _distribution_method_invoker