class_docstr: UNet Model inspired by the the original UNet paper
  
  Parameters
  ----------
  pretrained: bool (default=True)
      Option to use pretrained vgg16_bn based on ImageNet
  
  References
  ----------
  
  .. [1] Olaf Ronneberger, Philipp Fischer, Thomas Brox, 2015,
      "U-Net: Convolutional Networks for Biomedical Image Segmentation,".
      "MICCAI" `<https://arxiv.org/abs/1505.04597>`_
code: def __init__(self, pretrained=False):
      super().__init__()
      encoder = models.vgg16_bn(pretrained=pretrained).features
  
      self.conv1 = encoder[:6]
      self.conv2 = encoder[6:13]
      self.conv3 = encoder[13:23]
      self.conv4 = encoder[23:33]
      self.conv5 = encoder[33:43]
  
      self.center = nn.Sequential(
          encoder[43], make_decoder_block(512, 512, 256)  # MaxPool
      )
  
      self.dec5 = make_decoder_block(256 + 512, 512, 256)
      self.dec4 = make_decoder_block(256 + 512, 512, 256)
      self.dec3 = make_decoder_block(256 + 256, 256, 64)
      self.dec2 = make_decoder_block(64 + 128, 128, 32)
      self.dec1 = nn.Sequential(
          nn.Conv2d(32 + 64, 32, 3, padding=1), nn.ReLU(inplace=True)
      )
      self.final = nn.Conv2d(32, 1, kernel_size=1)
  
file_path: skorch/examples/nuclei_image_segmentation/model.py
imports: import torch
  import torch.nn
  from torchvision import models
label_desc: Use image node_id 1 to create a new UNet object from inherited base classes: nn with example: obj = UNet(pretrained)