code: def test_relaxed_bernoulli():
      class TestRelaxedBernoulli(HybridBlock):
          def __init__(self, func, is_logit=False):
              super(TestRelaxedBernoulli, self).__init__()
              self._is_logit = is_logit
              self._func = func
  
          def forward(self, params, *args):
              relaxed_bernoulli = (
                  mgp.RelaxedBernoulli(
                      T=1.0, logit=params, validate_args=True
                  )
                  if self._is_logit
                  else mgp.RelaxedBernoulli(
                      T=1.0, prob=params, validate_args=True
                  )
              )
              if self._func == "sample":
                  return relaxed_bernoulli.sample()
              return _distribution_method_invoker(
                  relaxed_bernoulli, self._func, *args
              )
  
      def prob_to_logit(prob):
          return np.log(prob) - np.log1p(-prob)
  
      shapes = [(), (1,), (2, 3), 6]
      # Test sampling
      for shape, hybridize, use_logit in itertools.product(
          shapes, [True, False], [True, False]
      ):
          prob = np.random.uniform(size=shape)
          param = prob
          if use_logit:
              param = prob_to_logit(param)
          param.attach_grad()
          net = TestRelaxedBernoulli("sample", use_logit)
          if hybridize:
              net.hybridize()
          with autograd.record():
              mx_out = net(param)
          mx_out.backward()
          desired_shape = (shape,) if isinstance(shape, int) else shape
          assert param.grad.shape == desired_shape
  
      for shape, hybridize, use_logit in itertools.product(
          shapes, [True, False], [True, False]
      ):
          prob = np.random.uniform(size=shape)
          sample = np.random.uniform(0.1, 0.9, size=shape)
          param = prob
          if use_logit:
              param = prob_to_logit(param)
          net = TestRelaxedBernoulli("log_prob", use_logit)
          if hybridize:
              net.hybridize()
          mx_out = net(param, sample).asnumpy()
          desired_shape = (shape,) if isinstance(shape, int) else shape
          assert mx_out.shape == desired_shape
  
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 23 for calling a global function with example usage: test_relaxed_bernoulli() and returns: unknown, _distribution_method_invoker, relaxed_bernoulli