code: def define_G(
      input_nc,
      output_nc,
      ngf,
      netG,
      n_downsample_global=3,
      n_blocks_global=9,
      n_local_enhancers=1,
      n_blocks_local=3,
      norm="instance",
      gpu_ids=[],
      last_op=nn.Tanh(),
  ):
      norm_layer = get_norm_layer(norm_type=norm)
      if netG == "global":
          netG = GlobalGenerator(
              input_nc,
              output_nc,
              ngf,
              n_downsample_global,
              n_blocks_global,
              norm_layer,
              last_op=last_op,
          )
      elif netG == "local":
          netG = LocalEnhancer(
              input_nc,
              output_nc,
              ngf,
              n_downsample_global,
              n_blocks_global,
              n_local_enhancers,
              n_blocks_local,
              norm_layer,
          )
      elif netG == "encoder":
          netG = Encoder(
              input_nc, output_nc, ngf, n_downsample_global, norm_layer
          )
      else:
          raise ("generator not implemented!")
      # print(netG)
      if len(gpu_ids) > 0:
          assert torch.cuda.is_available()
          netG.cuda(gpu_ids[0])
      netG.apply(weights_init)
      return netG
  
imports: import torch
  import torch.nn
  import functools
  from torch.autograd import Variable
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: define_G(input_nc, output_nc, ngf, netG, n_downsample_global, n_blocks_global, n_local_enhancers, n_blocks_local, norm, gpu_ids, last_op) and returns: netG