code: def test_gluon_stochastic_sequential():
      class normalBlock(HybridBlock):
          def forward(self, x):
              return x + 1
  
      class stochasticBlock(StochasticBlock):
          @StochasticBlock.collectLoss
          def forward(self, x):
              self.add_loss(x**2)
              self.add_loss(x - 1)
              return x + 1
  
      class problemBlock(StochasticBlock):
          def forward(self, x):
              self.add_loss(x**2)
              self.add_loss(x - 1)
              return x + 1
  
      shape = (4, 4)
      for hybridize in [True, False]:
          initial_value = np.ones(shape)
          net = StochasticSequential()
          net.add(stochasticBlock())
          net.add(normalBlock())
          net.add(stochasticBlock())
          net.add(normalBlock())
          if hybridize:
              net.hybridize()
          mx_out = net(initial_value).asnumpy()
          assert_almost_equal(mx_out, _np.ones(shape) * 5)
          accumulated_loss = net.losses
          assert len(accumulated_loss) == 2
          assert_almost_equal(
              accumulated_loss[0][0].asnumpy(), _np.ones(shape)
          )
          assert_almost_equal(
              accumulated_loss[0][1].asnumpy(), _np.ones(shape) - 1
          )
          assert_almost_equal(
              accumulated_loss[1][0].asnumpy(), _np.ones(shape) * 9
          )
          assert_almost_equal(
              accumulated_loss[1][1].asnumpy(), _np.ones(shape) + 1
          )
  
      for hybridize in [True, False]:
          initial_value = np.ones(shape)
          net = StochasticSequential()
          net.add(stochasticBlock())
          net.add(normalBlock())
          net.add(problemBlock())
          net.add(normalBlock())
          if hybridize:
              net.hybridize()
          with pytest.raises(ValueError):
              mx_out = net(initial_value).asnumpy()
  
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 36 for calling a global function with example usage: test_gluon_stochastic_sequential() and returns: unknown, unknown, unknown