code: def get_model(self, use_bn=False, out_type=None):
      class Model(chainer.Chain):
          def __init__(self, use_bn=False, out_type=None):
              super(Model, self).__init__()
  
              self._use_bn = use_bn
              self._out_type = out_type
              with self.init_scope():
                  self.conv = L.Convolution2D(
                      None, 32, ksize=3, stride=1
                  )
                  if self._use_bn:
                      self.bn = L.BatchNormalization(32)
  
          def __call__(self, x):
              h = self.conv(x)
              if self._use_bn:
                  h = self.bn(h)
              o1 = F.tanh(h)
              o2 = F.sigmoid(h)
              if self._out_type == "dict":
                  return {"tanh": o1, "sigmoid": o2}
              elif self._out_type == "tuple":
                  return o1, o2
              elif self._out_type == "list":
                  return [o1, o2]
  
      return Model(use_bn=use_bn, out_type=out_type)
  
imports: import unittest
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import testing
  import numpy
  import pytest
  from onnx_chainer import export
  from onnx_chainer.export import RetainInputHook
  from onnx_chainer.testing import input_generator
  from onnx_chainer_tests.helper import ONNXModelTest
label_desc: Use image node_id 1 for calling the TestMultipleOutput obj's underlying member method code with example usage: obj.get_model(use_bn, out_type) and returns: Model, dict, o1,  o2, list