code: def test_gluon_laplace():
      class TestLaplace(HybridBlock):
          def __init__(self, func):
              super(TestLaplace, self).__init__()
              self._func = func
  
          def forward(self, loc, scale, *args):
              laplace = mgp.Laplace(loc, scale, validate_args=True)
              return _distribution_method_invoker(
                  laplace, self._func, *args
              )
  
      shapes = [(), (1,), (2, 3), 6]
  
      # Test log_prob
      for shape, hybridize in itertools.product(shapes, [True, False]):
          loc = np.random.uniform(-1, 1, shape)
          scale = np.random.uniform(0.5, 1.5, shape)
          samples = np.random.laplace(size=shape)
          net = TestLaplace("log_prob")
          if hybridize:
              net.hybridize()
          mx_out = net(loc, scale, samples).asnumpy()
          np_out = ss.laplace(loc.asnumpy(), scale.asnumpy()).logpdf(
              samples.asnumpy()
          )
          assert_almost_equal(
              mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False
          )
  
      # Test cdf
      for shape, hybridize in itertools.product(shapes, [True, False]):
          loc = np.random.uniform(-1, 1, shape)
          scale = np.random.uniform(0.5, 1.5, shape)
          samples = np.random.laplace(size=shape)
          net = TestLaplace("cdf")
          if hybridize:
              net.hybridize()
          mx_out = net(loc, scale, samples).asnumpy()
          np_out = ss.laplace(loc.asnumpy(), scale.asnumpy()).cdf(
              samples.asnumpy()
          )
          assert_almost_equal(
              mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False
          )
  
      # Test icdf
      for shape, hybridize in itertools.product(shapes, [True, False]):
          loc = np.random.uniform(-1, 1, shape)
          scale = np.random.uniform(0.5, 1.5, shape)
          samples = np.random.uniform(size=shape)
          net = TestLaplace("icdf")
          if hybridize:
              net.hybridize()
          mx_out = net(loc, scale, samples).asnumpy()
          np_out = ss.laplace(loc.asnumpy(), scale.asnumpy()).ppf(
              samples.asnumpy()
          )
          assert_almost_equal(
              mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False
          )
  
      # Test entropy
      for shape, hybridize in itertools.product(shapes, [True, False]):
          loc = np.random.uniform(-1, 1, shape)
          scale = np.random.uniform(0.5, 1.5, shape)
          net = TestLaplace("entropy")
          if hybridize:
              net.hybridize()
          mx_out = net(loc, scale).asnumpy()
          np_out = ss.laplace(loc.asnumpy(), scale.asnumpy()).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 5 for calling a global function with example usage: test_gluon_laplace() and returns: _distribution_method_invoker