class_docstr: PyTorch Hub interface for classy vision tasks and models.
  
  The task is optional, but a model is guaranteed to be present.  Do
  not use the constructor directly, instead Use from_task() or
  from_model() to instantiate the class.
  
  See the examples folder for an example of how to use this class
  
  Attributes:
      task: If present, task that can be used to train the torchhub model
      model: torchub model
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docstr: Returns an iterator that can be used to retrieve training / testing samples.
  
  Args:
      dataset: Dataset to iterate over
docstr_tok: ["Returns","an","iterator","that","can","be","used","to","retrieve","training","\/","testing","samples",".","Args",":","dataset",":","Dataset","to","iterate","over"]
code: def get_data_iterator(dataset: ClassyDataset) -> Iterator[Any]:
      """Returns an iterator that can be used to retrieve training / testing samples.
  
      Args:
          dataset: Dataset to iterate over
      """
      return iter(dataset.iterator())
  
file_path: ClassyVision/classy_vision/hub/classy_hub_interface.py
imports: from typing import Any, Callable, Iterator, List, Optional, Union
  import torch
  import torch.nn
  from classy_vision.dataset import ClassyDataset
  from classy_vision.dataset.image_path_dataset import ImagePathDataset
  from classy_vision.dataset.transforms import ClassyTransform
  from classy_vision.dataset.transforms.util import build_field_transform_default_imagenet
  from classy_vision.models import ClassyModel
  from classy_vision.tasks import ClassyTask
label_desc: Use image node_id 5 for calling the ClassyHubInterface obj's underlying member method code with example usage: obj.get_data_iterator(dataset) and returns: iter