class_docstr: A-to-B datasets [ZPI+17]_
  
  A general class for image-to-image translation dataset. Currently
  supported are:
  
  * Horse <-> Zebra
  * Apple <-> Orange
  * Summer <-> Winter
  
  .. [ZPI+17] Zhu, J., Park, T., Isola, P., & Efros, A.A. (2017).
      Unpaired Image-to-Image Translation Using Cycle-Consistent
      Adversarial Networks. 2017 IEEE International Conference on
      Computer Vision (ICCV), 2242-2251.
class_docstr_tok: ["A-to-B","datasets","[","ZPI+17","]","_","A","general","class","for","image-to-image","translation","dataset",".","Currently","supported","are",":","*","Horse","<","-",">","Zebra","*","Apple","<","-",">","Orange","*","Summer","<","-",">","Winter","..","[","ZPI+17","]","Zhu",",","J.",",","Park",",","T.",",","Isola",",","P.",",","&","Efros",",","A.A.","(","2017",")",".","Unpaired","Image-to-Image","Translation","Using","Cycle-Consistent","Adversarial","Networks",".","2017","IEEE","International","Conference","on","Computer","Vision","(","ICCV",")",",","2242-2251","."]
docstr_tok: []
code: def get_model(self, arch, pretrained=False):
      """ """
      if pretrained:
          raise ValueError(
              "A2B does not support pytorch_pretrained=True"
          )
      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 A2B obj's underlying member method code with example usage: obj.get_model(arch, pretrained) and returns: imagenet_models