class_docstr: CustomImagenet Dataset 
  
  A subset of ImageNet with the user-specified labels
  
  To initialize, just provide the path to the full ImageNet dataset
  along with a list of lists of wnids to be grouped together
  (no special formatting required).
class_docstr_tok: ["CustomImagenet","Dataset","A","subset","of","ImageNet","with","the","user-specified","labels","To","initialize",",","just","provide","the","path","to","the","full","ImageNet","dataset","along","with","a","list","of","lists","of","wnids","to","be","grouped","together","(","no","special","formatting","required",")","."]
docstr_tok: []
code: def get_model(self, arch, pretrained):
      """ """
      if pretrained:
          raise ValueError("Dataset doesn't support pytorch_pretrained")
      return imagenet_models.__dict__[arch](
          num_classes=self.num_classes
      )
  
imports: import pathlib
  import torch
  import torch.utils.data
  from .None import imagenet_models, cifar_models
  from torchvision import transforms, datasets
  from .tools import constants
  from .tools import openimgs_helpers
  from .None import data_augmentation
  from .None import loaders
  from .tools.helpers import get_label_mapping
label_desc: Use image node_id 2 for calling the CustomImageNet obj's underlying member method code with example usage: obj.get_model(arch, pretrained) and returns: imagenet_models