code: def get_model(
      model_name, nchannels=3, imsize=32, nclasses=10, half=False
  ):
      ngpus = torch.cuda.device_count()
  
      print("=> creating model '{}'".format(model_name))
      if imsize < 128 and model_name in models.__dict__:
          model = models.__dict__[model_name](
              num_classes=nclasses, nchannels=nchannels
          )
      else:
          model = models.__dict__[model_name](
              num_classes=nclasses, nchannels=nchannels
          )
      model = nn.DataParallel(model).cuda()
      cudnn.benchmark = True
      if half:
          print("Using half precision except in Batch Normalization!")
          model = model.half()
          for module in model.modules():
              if isinstance(module, nn.BatchNorm2d) or isinstance(
                  module, nn.BatchNorm1d
              ):
                  module.float()
      return model
  
imports: import collections
  import torch
  from torch import Tensor
  import torch.nn
  import torch.backends.cudnn
  import subprocess
  import os
  import time
  import shutil
  from datetime import datetime
  import torch.optim
  from torch.optim import lr_scheduler
  import sys
  import models
  import numpy
  import torch.nn
  import math
label_desc: Use image node_id 2 for calling a global function with example usage: get_model(model_name, nchannels, imsize, nclasses, half) and returns: model