code: def _get_untrained_model(model_name, num_classes):
      """
      Primarily, this method exists to return an untrained / vanilla version of a specified (pretrained) model.
      This is on best-attempt basis only and may be out of sync with actual model definitions. The code is manually maintained.
  
      :param model_name: Lower-case model names are pretrained by convention.
      :param num_classes: Number of classes to initialize the vanilla model with.
  
      :return: default model for the model_name with custom number of classes
      """
  
      if model_name.startswith("bninception"):
          return classification.BNInception(num_classes=num_classes)
      elif model_name.startswith("densenet"):
          return torch_models.DenseNet(num_classes=num_classes)
      elif model_name.startswith("dpn"):
          return classification.DPN(num_classes=num_classes)
      elif model_name.startswith("inceptionresnetv2"):
          return classification.InceptionResNetV2(
              num_classes=num_classes
          )
      elif model_name.startswith("inception_v3"):
          return torch_models.Inception3(num_classes=num_classes)
      elif model_name.startswith("inceptionv4"):
          return classification.InceptionV4(num_classes=num_classes)
      elif model_name.startswith("nasnetalarge"):
          return classification.NASNetALarge(num_classes=num_classes)
      elif model_name.startswith("nasnetamobile"):
          return classification.NASNetAMobile(num_classes=num_classes)
      elif model_name.startswith("pnasnet5large"):
          return classification.PNASNet5Large(num_classes=num_classes)
      elif model_name.startswith("polynet"):
          return classification.PolyNet(num_classes=num_classes)
      elif model_name.startswith("pyresnet"):
          return classification.PyResNet(num_classes=num_classes)
      elif model_name.startswith("resnet"):
          return torch_models.ResNet(
              Bottleneck, [3, 4, 23, 3], num_classes=num_classes
          )
      elif model_name.startswith("resnext101_32x4d"):
          return classification.ResNeXt101_32x4d(
              num_classes=num_classes
          )
      elif model_name.startswith("resnext101_64x4d"):
          return classification.ResNeXt101_64x4d(
              num_classes=num_classes
          )
      elif model_name.startswith("se_inception"):
          return classification.SEInception3(num_classes=num_classes)
      elif model_name.startswith("se_resnext50_32x4d"):
          return classification.se_resnext50_32x4d(
              num_classes=num_classes, pretrained=None
          )
      elif model_name.startswith("se_resnext101_32x4d"):
          return classification.se_resnext101_32x4d(
              num_classes=num_classes, pretrained=None
          )
      elif model_name.startswith("senet154"):
          return classification.senet154(
              num_classes=num_classes, pretrained=None
          )
      elif model_name.startswith("se_resnet50"):
          return classification.se_resnet50(
              num_classes=num_classes, pretrained=None
          )
      elif model_name.startswith("se_resnet101"):
          return classification.se_resnet101(
              num_classes=num_classes, pretrained=None
          )
      elif model_name.startswith("se_resnet152"):
          return classification.se_resnet152(
              num_classes=num_classes, pretrained=None
          )
      elif model_name.startswith("squeezenet1_0"):
          return torch_models.squeezenet1_0(
              num_classes=num_classes, pretrained=False
          )
      elif model_name.startswith("squeezenet1_1"):
          return torch_models.squeezenet1_1(
              num_classes=num_classes, pretrained=False
          )
      elif model_name.startswith("xception"):
          return classification.Xception(num_classes=num_classes)
      else:
          raise ValueError(
              "No vanilla model found for model name: {}".format(
                  model_name
              )
          )
  
imports: from functools import partial
  from typing import Callable
  from torchvision.models.resnet import Bottleneck
  from .None import classification
  from .segmentation import *
  from .None import segmentation
  from enum import Enum
  from torchvision import models
  from torchvision.models.inception import InceptionAux
  import torch
  import torch.nn
  import os
  import errno
label_desc: Use image node_id 4 for calling a global function with example usage: _get_untrained_model(model_name, num_classes) and returns: classification, torch_models, classification, classification, torch_models, classification, classification, classification, classification, classification, classification, torch_models, classification, classification, classification, classification, classification, classification, classification, classification, classification, torch_models, torch_models, classification