code: def build_net(
      arch="AFN1",
      num_input=4,
      num_classes=5,
      num_branches=4,
      padding_list=None,
      dilation_list=None,
      **kwargs,
  ):
      if padding_list is None:
          padding_list = [0, 4, 8, 12]
      if dilation_list is None:
          dilation_list = [2, 6, 10, 14]
      # parameters in the architecture
      channels = [
          num_input - 1,
          30,
          30,
          40,
          40,
          40,
          40,
          50,
          50,
          num_classes,
      ]
      kernel_size = 3
  
      # Baselines
      if arch == "Basic":
          network = Basic(channels, kernel_size)
          return network
      elif arch == "ASPP_c":
          network = ASPP_c(
              dilation_list, channels, kernel_size, num_branches
          )
          return network
      elif arch == "ASPP_s":
          network = ASPP_s(
              dilation_list, channels, kernel_size, num_branches
          )
          return network
  
      # Autofocus Neural Networks
      elif arch == "AFN1":
          blocks = [BasicBlock, BasicBlock, Autofocus_single]
      elif arch == "AFN2":
          blocks = [BasicBlock, BasicBlock, Autofocus]
      elif arch == "AFN3":
          blocks = [BasicBlock, Autofocus_single, Autofocus]
      elif arch == "AFN4":
          blocks = [BasicBlock, Autofocus, Autofocus]
      elif arch == "AFN5":
          blocks = [Autofocus_single, Autofocus, Autofocus]
      elif arch == "AFN6":
          blocks = [Autofocus, Autofocus, Autofocus]
      else:
          raise Exception("Architecture undefined")
  
      network = AFN(
          blocks,
          padding_list,
          dilation_list,
          channels,
          kernel_size,
          num_branches,
      )
      return network
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 for calling the ModelBuilder obj's underlying member method code with example usage: obj.build_net(arch, num_input, num_classes, num_branches, padding_list, dilation_list) and returns: network, network, network, network