code: def load_model(
      model_type: ModelType,
      model_name: str,
      num_classes: int,
      pretrained: bool = True,
      **kwargs,
  ):
      """
      Certain timm models may exist but not be listed in torch.hub so uses a custom partial function to bypass the model check in pywick
  
      :param model_type:
      :param model_name:
      :param num_classes:
      :param pretrained:
      :param kwargs:
      :return:
      """
      custom_func = partial(torch.hub.load, github=rwightman_repo)
      model = get_model(
          model_type=model_type,
          model_name=model_name,
          num_classes=num_classes,
          pretrained=pretrained,
          custom_load_fn=custom_func,
          **kwargs,
      )
  
      return model
  
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 7 for calling a global function with example usage: load_model(model_type, model_name, num_classes, pretrained) and returns: model