code: def wideresnet50(pooling):
      dir_models = os.path.join(expanduser("~"), ".torch/wideresnet")
      path_hkl = os.path.join(dir_models, "wideresnet50.hkl")
      if os.path.isfile(path_hkl):
          params = hkl.load(path_hkl)
          # convert numpy arrays to torch Variables
          for k, v in sorted(params.items()):
              print(k, v.shape)
              params[k] = Variable(
                  torch.from_numpy(v), requires_grad=True
              )
      else:
          os.system("mkdir -p " + dir_models)
          os.system(
              "wget {} -O {}".format(
                  model_urls["wideresnet50"], path_hkl
              )
          )
      f = define_model(params)
      model = WideResNet(pooling)
      return model
  
file_path: pretrained-models.pytorch/pretrainedmodels/models/wideresnet.py
imports: from __future__ import print_function, division, absolute_import
  import os
  from os.path import expanduser
  import hickle
  import torch
  import torch.nn.functional
  from torch.autograd import Variable
label_desc: Use image node_id 2 for calling a global function with example usage: wideresnet50(pooling) and returns: model