class_docstr: implementation of HighRes3DNet:
  
      Li et al., "On the compactness, efficiency, and representation of 3D
      convolutional networks: Brain parcellation as a pretext task", IPMI '17
  
  (This is smaller model with an initial stride-2 convolution)
  
  ### Constraints
  - Input image size should be divisible by 8
class_docstr_tok: ["implementation","of","HighRes3DNet",":","Li","et","al.",",","``","On","the","compactness",",","efficiency",",","and","representation","of","3D","convolutional","networks",":","Brain","parcellation","as","a","pretext","task","''",",","IPMI","'17","(","This","is","smaller","model","with","an","initial","stride-2","convolution",")","#","#","#","Constraints","-","Input","image","size","should","be","divisible","by","8"]
code: def _print(self, list_of_layers):
      for op, _ in list_of_layers:
          print(op)
  
file_path: NiftyNet/niftynet/network/highres3dnet_small.py
imports: from __future__ import absolute_import, print_function
  from six.moves import range
  from niftynet.layer import layer_util
  from niftynet.layer.convolution import ConvolutionalLayer
  from niftynet.layer.deconvolution import DeconvolutionalLayer
  from niftynet.layer.dilatedcontext import DilatedTensor
  from niftynet.network.base_net import BaseNet
  from niftynet.network.highres3dnet import HighResBlock
label_desc: Use image node_id 3 for calling the HighRes3DNetSmall obj's underlying member method code with example usage: obj._print(list_of_layers) without return types