code: def test_gluon_gamma():
      class TestGamma(HybridBlock):
          def __init__(self, func):
              super(TestGamma, self).__init__()
              self._func = func
  
          def forward(self, shape, scale, *args):
              gamma = mgp.Gamma(shape, scale, validate_args=True)
              return _distribution_method_invoker(
                  gamma, self._func, *args
              )
  
      shapes = [(), (1,), (2, 3), 6]
  
      # Test log_prob
      for shape, hybridize in itertools.product(shapes, [True, False]):
          alpha = np.random.uniform(0.5, 1.5, shape)
          scale = np.random.uniform(0.5, 1.5, shape)
          samples = np.random.uniform(size=shape)
          net = TestGamma("log_prob")
          if hybridize:
              net.hybridize()
          mx_out = net(alpha, scale, samples).asnumpy()
          np_out = ss.gamma(
              a=alpha.asnumpy(), loc=0, scale=scale.asnumpy()
          ).logpdf(samples.asnumpy())
          assert_almost_equal(
              mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False
          )
  
      # Test `mean`, `var` and `entropy`
      for shape, hybridize in itertools.product(shapes, [True, False]):
          for func in ["mean", "variance", "entropy"]:
              alpha = np.random.uniform(0.5, 1.5, shape)
              scale = np.random.uniform(0.5, 1.5, shape)
              net = TestGamma(func)
              if hybridize:
                  net.hybridize()
              mx_out = net(alpha, scale).asnumpy()
              ss_gamma = ss.gamma(
                  a=alpha.asnumpy(), loc=0, scale=scale.asnumpy()
              )
              if func == "mean":
                  np_out = ss_gamma.mean()
              elif func == "variance":
                  np_out = ss_gamma.var()
              else:
                  np_out = ss_gamma.entropy()
              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 14 for calling a global function with example usage: test_gluon_gamma() and returns: _distribution_method_invoker