class_docstr: Base class for representing a dataset. Meant to be subclassed, with
  subclasses implementing the `get_model` function. 
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docstr: Should be overriden by subclasses. Also, you will probably never
  need to call this function, and should instead by using
  `model_utils.make_and_restore_model </source/robustness.model_utils.html>`_.
  
  Args:
      arch (str) : name of architecture 
      pretrained (bool): whether to try to load torchvision 
          pretrained checkpoint
  
  Returns:
      A model with the given architecture that works for each
      dataset (e.g. with the right input/output dimensions).
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code: def get_model(self, arch, pretrained):
      """
      Should be overriden by subclasses. Also, you will probably never
      need to call this function, and should instead by using
      `model_utils.make_and_restore_model </source/robustness.model_utils.html>`_.
  
      Args:
          arch (str) : name of architecture
          pretrained (bool): whether to try to load torchvision
              pretrained checkpoint
  
      Returns:
          A model with the given architecture that works for each
          dataset (e.g. with the right input/output dimensions).
      """
  
      raise NotImplementedError
  
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 3 for calling the DataSet obj's underlying member method code with example usage: obj.get_model(arch, pretrained) without return types