class_docstr: AudioIODataset
docstr: AudioIODataset.
docstr_tok: ["AudioIODataset","."]
code: def __init__(self, filename, dtype=None):
      """AudioIODataset."""
      with tf.name_scope("AudioIODataset"):
          if not tf.executing_eagerly():
              assert (
                  dtype is not None
              ), "dtype must be provided in graph mode"
          resource = core_ops.io_audio_readable_init(filename)
          if tf.executing_eagerly():
              shape, dtype, _ = core_ops.io_audio_readable_spec(
                  resource
              )
              dtype = tf.as_dtype(dtype.numpy())
          else:
              shape, _, _ = core_ops.io_audio_readable_spec(resource)
  
          capacity = 1024  # kwargs.get("capacity", 4096)
  
          self._resource = resource
          dataset = tf.data.Dataset.range(0, shape[0], capacity)
          dataset = dataset.map(
              lambda index: core_ops.io_audio_readable_read(
                  resource, index, index + capacity, dtype=dtype
              )
          )
          dataset = dataset.apply(
              tf.data.experimental.take_while(
                  lambda v: tf.greater(tf.shape(v)[0], 0)
              )
          )
          dataset = dataset.unbatch()
          self._dataset = dataset
          super().__init__(self._dataset._variant_tensor)
  
imports: import sys
  import tensorflow
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 1 to create a new AudioIODataset object from inherited base classes: tf.data with example: obj = AudioIODataset(filename, dtype)