code: def test_gluon_beta():
      class TestBeta(HybridBlock):
          def __init__(self, func):
              super(TestBeta, self).__init__()
              self._func = func
  
          def forward(self, alpha, beta, *args):
              beta_dist = mgp.Beta(alpha, beta, validate_args=True)
              return _distribution_method_invoker(
                  beta_dist, 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)
          beta = np.random.uniform(0.5, 1.5, shape)
          samples = np.random.uniform(size=shape)
          net = TestBeta("log_prob")
          if hybridize:
              net.hybridize()
          mx_out = net(alpha, beta, samples).asnumpy()
          np_out = ss.beta(alpha.asnumpy(), beta.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)
              beta = np.random.uniform(0.5, 1.5, shape)
              net = TestBeta(func)
              if hybridize:
                  net.hybridize()
              mx_out = net(alpha, beta).asnumpy()
              ss_beta = ss.beta(alpha.asnumpy(), beta.asnumpy())
              if func == "mean":
                  np_out = ss_beta.mean()
              elif func == "variance":
                  np_out = ss_beta.var()
              else:
                  np_out = ss_beta.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 16 for calling a global function with example usage: test_gluon_beta() and returns: _distribution_method_invoker