code: def test_mixture_of_experts_problem_with_specific_network(self):
      with self.assertRaisesRegexp(ValueError, "specified as a list"):
          architectures.mixture_of_experts(*self.networks)
  
      with self.assertRaisesRegexp(
          ValueError, "has more than one input"
      ):
          last_network = layers.join(
              layers.parallel(
                  layers.Input(1),
                  layers.Input(2),
              ),
              layers.Concatenate(),
          )
          architectures.mixture_of_experts(
              networks=self.networks + [last_network]
          )
  
      with self.assertRaisesRegexp(
          ValueError, "has more than one output"
      ):
          last_network = layers.join(
              layers.Input(1),
              layers.parallel(
                  layers.Softmax(1),
                  layers.Softmax(1),
              ),
          )
          architectures.mixture_of_experts(
              networks=self.networks + [last_network]
          )
  
      error_message = (
          "Each network from the mixture of experts has to "
          "process only 2-dimensional inputs. Network #2.+"
          "Input layer's shape: \(\?, 1, 1, 1\)"
      )
      with self.assertRaisesRegexp(ValueError, error_message):
          last_network = layers.Input((1, 1, 1))
          architectures.mixture_of_experts(
              networks=self.networks + [last_network]
          )
  
file_path: neupy/tests/architectures/test_mixtures_of_experts.py
imports: import copy
  import numpy
  from sklearn import datasets, preprocessing, model_selection
  from neupy.utils import asfloat
  from neupy.exceptions import LayerConnectionError
  from neupy.algorithms.gd import objectives
  from neupy import algorithms, layers, architectures
  from base import BaseTestCase
label_desc: Use image node_id 2 for calling the MixtureOfExpertsTestCase obj's underlying member method code with example usage: obj.test_mixture_of_experts_problem_with_specific_network() without return types