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 forward(self, x):
      conv1 = self.conv1(x)
      conv2 = self.conv2(conv1)
      conv3 = self.conv3(conv2)
      conv4 = self.conv4(conv3)
      conv5 = self.conv5(conv4)
  
      center = self.center(conv5)
  
      dec5 = self.dec5(torch.cat([center, conv5], 1))
      dec4 = self.dec4(torch.cat([dec5, conv4], 1))
      dec3 = self.dec3(torch.cat([dec4, conv3], 1))
      dec2 = self.dec2(torch.cat([dec3, conv2], 1))
      dec1 = self.dec1(torch.cat([dec2, conv1], 1))
  
      return self.final(dec1)
  
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 2 for calling the UNet obj's underlying member method code with example usage: obj.forward(x) and returns: self