class_docstr: A reference of 3D U-Net model.
  
  Implementation origin :
  https://github.com/shiba24/3d-unet/blob/master/pytorch/model.py
  
  .. seealso::
      Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox
      and Olaf Ronneberger (2016). 3D U-Net: Learning Dense Volumetric
      Segmentation from Sparse Annotation
      ArXiv link: https://arxiv.org/pdf/1606.06650.pdf
class_docstr_tok: ["A","reference","of","3D","U-Net","model",".","Implementation","origin",":","https",":","\/\/github.com\/shiba24\/3d-unet\/blob\/master\/pytorch\/model.py","..","seealso",":",":","\u00d6zg\u00fcn","\u00c7i\u00e7ek",",","Ahmed","Abdulkadir",",","Soeren","S.","Lienkamp",",","Thomas","Brox","and","Olaf","Ronneberger","(","2016",")",".","3D","U-Net",":","Learning","Dense","Volumetric","Segmentation","from","Sparse","Annotation","ArXiv","link",":","https",":","\/\/arxiv.org\/pdf\/1606.06650.pdf"]
code: def down_conv(
      self,
      in_channels,
      out_channels,
      kernel_size=3,
      stride=1,
      padding=1,
      bias=True,
      batchnorm=False,
  ):
      if batchnorm:
          layer = nn.Sequential(
              nn.Conv3d(
                  in_channels,
                  out_channels,
                  kernel_size,
                  stride=stride,
                  padding=padding,
                  bias=bias,
              ),
              nn.BatchNorm2d(out_channels),
              nn.LeakyReLU(),
          )
      else:
          layer = nn.Sequential(
              nn.Conv3d(
                  in_channels,
                  out_channels,
                  kernel_size,
                  stride=stride,
                  padding=padding,
                  bias=bias,
              ),
              nn.LeakyReLU(),
          )
      return layer
  
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 UNet3D obj's underlying member method code with example usage: obj.down_conv(in_channels, out_channels, kernel_size, stride, padding, bias, batchnorm) and returns: layer