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 __init__(self, data_path="/tmp/", **kwargs):
      """ """
      ds_kwargs = {
          "num_classes": 10,
          "mean": ch.tensor([0.4914, 0.4822, 0.4465]),
          "std": ch.tensor([0.2023, 0.1994, 0.2010]),
          "custom_class": datasets.CIFAR10,
          "label_mapping": None,
          "transform_train": da.TRAIN_TRANSFORMS_DEFAULT(32),
          "transform_test": da.TEST_TRANSFORMS_DEFAULT(32),
      }
      ds_kwargs = self.override_args(ds_kwargs, kwargs)
      super(CIFAR, self).__init__("cifar", data_path, **ds_kwargs)
  
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 1 to create a new CIFAR object from inherited base classes: DataSet with example: obj = CIFAR(data_path)