code: def get_resnet(version, num_layers, pretrained=False, ctx=cpu(),
                 root=os.path.join(base.data_dir(), 'models'), **kwargs):
      r
      assert num_layers in resnet_spec, \
          "Invalid number of layers: %d. Options are %s"%(
              num_layers, str(resnet_spec.keys()))
      block_type, layers, channels = resnet_spec[num_layers]
      assert version >= 1 and version <= 2, \
          "Invalid resnet version: %d. Options are 1 and 2."%version
      resnet_class = resnet_net_versions[version-1]
      block_class = resnet_block_versions[version-1][block_type]
      net = resnet_class(block_class, layers, channels, **kwargs)
      if pretrained:
          from ..model_store import get_model_file
          net.load_parameters(get_model_file('resnet%d_v%d'%(num_layers, version),
                                             root=root), ctx=ctx)
      return net
docs: r"""ResNet V1 model from `"Deep Residual Learning for Image Recognition"
      <http://arxiv.org/abs/1512.03385>`_ paper.
      ResNet V2 model from `"Identity Mappings in Deep Residual Networks"
      <https://arxiv.org/abs/1603.05027>`_ paper.
  
      Parameters
      ----------
      version : int
          Version of ResNet. Options are 1, 2.
      num_layers : int
          Numbers of layers. Options are 18, 34, 50, 101, 152.
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default $MXNET_HOME/models
          Location for keeping the model parameters.