class_docstr: CIFAR-10 dataset [Kri09]_.
  
  A dataset with 50k training images and 10k testing images, with the
  following classes:
  
  * Airplane
  * Automobile
  * Bird
  * Cat
  * Deer
  * Dog
  * Frog
  * Horse
  * Ship
  * Truck
  
  .. [Kri09] Krizhevsky, A (2009). Learning Multiple Layers of Features
      from Tiny Images. Technical Report.
class_docstr_tok: ["CIFAR-10","dataset","[","Kri09","]","_",".","A","dataset","with","50k","training","images","and","10k","testing","images",",","with","the","following","classes",":","*","Airplane","*","Automobile","*","Bird","*","Cat","*","Deer","*","Dog","*","Frog","*","Horse","*","Ship","*","Truck","..","[","Kri09","]","Krizhevsky",",","A","(","2009",")",".","Learning","Multiple","Layers","of","Features","from","Tiny","Images",".","Technical","Report","."]
docstr_tok: []
code: def get_model(self, arch, pretrained):
      """ """
      if pretrained:
          raise ValueError(
              "CIFAR does not support pytorch_pretrained=True"
          )
      return cifar_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 CIFAR obj's underlying member method code with example usage: obj.get_model(arch, pretrained) and returns: cifar_models