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 __init__(self, drop_rate=0.4, bn_momentum=0.1):
      super(Unet, self).__init__()
  
      # Downsampling path
      self.conv1 = DownConv(1, 64, drop_rate, bn_momentum)
      self.mp1 = nn.MaxPool2d(2)
  
      self.conv2 = DownConv(64, 128, drop_rate, bn_momentum)
      self.mp2 = nn.MaxPool2d(2)
  
      self.conv3 = DownConv(128, 256, drop_rate, bn_momentum)
      self.mp3 = nn.MaxPool2d(2)
  
      # Bottom
      self.conv4 = DownConv(256, 256, drop_rate, bn_momentum)
  
      # Upsampling path
      self.up1 = UpConv(512, 256, drop_rate, bn_momentum)
      self.up2 = UpConv(384, 128, drop_rate, bn_momentum)
      self.up3 = UpConv(192, 64, drop_rate, bn_momentum)
  
      self.conv9 = nn.Conv2d(64, 1, kernel_size=3, padding=1)
  
imports: import torch
  import torch.nn
  from torch.nn import Module
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Unet object from inherited base classes: Module with example: obj = Unet(drop_rate, bn_momentum)