class_docstr: A reference U-Net model.
  
  .. seealso::
      Ronneberger, O., et al (2015). U-Net: Convolutional
      Networks for Biomedical Image Segmentation
      ArXiv link: https://arxiv.org/abs/1505.04597
class_docstr_tok: ["A","reference","U-Net","model",".","..","seealso",":",":","Ronneberger",",","O.",",","et","al","(","2015",")",".","U-Net",":","Convolutional","Networks","for","Biomedical","Image","Segmentation","ArXiv","link",":","https",":","\/\/arxiv.org\/abs\/1505.04597"]
code: def forward(self, x):
      x1 = self.conv1(x)
      x2 = self.mp1(x1)
  
      x3 = self.conv2(x2)
      x4 = self.mp2(x3)
  
      x5 = self.conv3(x4)
      x6 = self.mp3(x5)
  
      # Bottom
      x7 = self.conv4(x6)
  
      # Up-sampling
      x8 = self.up1(x7, x5)
      x9 = self.up2(x8, x3)
      x10 = self.up3(x9, x1)
  
      x11 = self.conv9(x10)
      preds = torch.sigmoid(x11)
  
      return preds
  
imports: import torch
  import torch.nn
  from torch.nn import Module
  import torch.nn.functional
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: preds