code: def __init__(
      self,
      elu=False,
      in_channels=1,
      num_classes=2,
      pretrained=None,
      kernel_size=((2, 2, 2), (2, 2, 2), (2, 2, 2), (2, 2, 2)),
      stride_size=((2, 2, 2), (2, 2, 2), (2, 2, 2), (2, 2, 2)),
      norm_layer=nn.BatchNorm3D,
      **kwargs,
  ):
      super().__init__(norm_layer=norm_layer, **kwargs)
  
      self.feature_extractor = VNet(
          elu=elu,
          in_channels=in_channels,
          num_classes=num_classes,
          pretrained=pretrained,
          kernel_size=kernel_size,
          stride_size=stride_size,
      )
  
imports: import os
  import sys
  import paddle
  import paddle.nn
  import numpy
  from inference.ops import DistMaps3D, ScaleLayer, BatchImageNormalize3D
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new VNetModel object from inherited base classes: ISModel3D with example: obj = VNetModel(elu, in_channels, num_classes, pretrained, kernel_size, stride_size, norm_layer)