code: def resnext50_32x4d(num_classes=1000, pretrained="imagenet"):
      """Pretrained Resnext50_32x4d model"""
      model = ResNeXt50_32x4d(num_classes=num_classes)
      if pretrained is not None:
          settings = pretrained_settings["resnext50_32x4d"][pretrained]
          if num_classes != settings["num_classes"]:
              raise AssertionError(
                  "num_classes should be {}, but is {}".format(
                      settings["num_classes"], num_classes
                  )
              )
          model.load_state_dict(model_zoo.load_url(settings["url"]))
          model.input_space = settings["input_space"]
          model.input_size = settings["input_size"]
          model.input_range = settings["input_range"]
          model.mean = settings["mean"]
          model.std = settings["std"]
      return model
  
imports: import torch.nn
  import torch.utils.model_zoo
  from .resnext_features import resnext50_32x4d_features
  from .resnext_features import resnext101_32x4d_features
  from .resnext_features import resnext101_64x4d_features
label_desc: Use image node_id 1 for calling a global function with example usage: resnext50_32x4d(num_classes, pretrained) and returns: model