class_docstr: Base class for input data adapters.
  
  The purpose of a DataAdapter is to provide a unfied interface to
  iterate over input data provided in a variety of formats -- such as
  NumPy arrays, tf.Tensors, tf.data.Datasets, Keras PyDatasets, etc.
docstr: Get a `tf.data.Dataset` instance for the DataAdapter.
  
  Note that the dataset returned does not repeat for epoch, so caller
  might need to create new iterator for the same dataset at the beginning
  of the epoch. This behavior might change in the future.
  
  Returns:
      A `tf.data.Dataset`. Caller might use the dataset in different
      context, e.g. iter(dataset) in eager to get the value directly, or
      in graph mode, provide the iterator tensor to Keras model function.
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code: def get_tf_dataset(self):
      """Get a `tf.data.Dataset` instance for the DataAdapter.
  
      Note that the dataset returned does not repeat for epoch, so caller
      might need to create new iterator for the same dataset at the beginning
      of the epoch. This behavior might change in the future.
  
      Returns:
          A `tf.data.Dataset`. Caller might use the dataset in different
          context, e.g. iter(dataset) in eager to get the value directly, or
          in graph mode, provide the iterator tensor to Keras model function.
      """
      raise NotImplementedError
  
file_path: keras/keras/trainers/data_adapters/data_adapter.py
label_desc: Use image node_id 2 for calling the DataAdapter obj's underlying member method code with example usage: obj.get_tf_dataset() without return types