class_docstr: Imagenette TFDS dataset.
  
  See https://www.tensorflow.org/datasets/catalog/imagenette for details.
  Images are at low quality and by default load 100 examples from the validation
  set though this can be changed through the `split` parameter, as per TFDS
  documentation.
code: def __init__(self, split: str = "validation[:100]"):
      tfds_examples = tfds.as_numpy(
          tfds.load(
              "imagenette/160px",
              split=split,
              download=True,
              try_gcs=True,
          )
      )
  
      def convert_input(inp):
          pil_image = PILImage.fromarray(inp["image"])
          image_str = image_utils.convert_pil_to_image_str(pil_image)
          return {"image": image_str}
  
      self._examples = [convert_input(inp) for inp in tfds_examples]
  
file_path: lit/lit_nlp/examples/datasets/imagenette.py
imports: from lit_nlp.api import dataset
  from lit_nlp.api import types
  from lit_nlp.lib import image_utils
  from PIL import Image
  import tensorflow_datasets
label_desc: Use image node_id 1 to create a new ImagenetteDataset object from inherited base classes: dataset with example: obj = ImagenetteDataset(split)