class_docstr: HDF5IODataset
docstr: HDF5IODataset.
docstr_tok: ["HDF5IODataset","."]
code: def __init__(self, filename, dataset, spec=None, internal=True):
      """HDF5IODataset."""
      with tf.name_scope("HDF5IODataset"):
          assert internal
  
          components, shapes, dtypes = core_ops.io_hdf5_readable_info(
              filename, shared=filename, container="HDF5IODataset"
          )
          shape = tf.boolean_mask(
              shapes, tf.math.equal(components, dataset)
          )[0]
          shape = tf.boolean_mask(
              shape, tf.math.greater_equal(shape, 0)
          )
  
          if tf.executing_eagerly():
              dtype = tf.boolean_mask(
                  dtypes, tf.math.equal(components, dataset)
              )[0]
              dtype = tf.as_dtype(dtype.numpy())
          else:
              assert spec is not None
              dtype = (
                  spec
                  if isinstance(spec, tf.dtypes.DType)
                  else spec.dtype
              )
  
          self._filename = filename
          self._component = dataset
          self._shape = shape
          self._dtype = dtype
  
          step = 1024
          indices_start = tf.data.Dataset.range(0, shape[0], step)
          indices_stop = indices_start.skip(1).concatenate(
              tf.data.Dataset.from_tensor_slices([shape[0]])
          )
          dataset = tf.data.Dataset.zip((indices_start, indices_stop))
  
          def f(start, stop):
              return core_ops.io_hdf5_readable_read(
                  input=self._filename,
                  shared=self._filename,
                  component=self._component,
                  shape=self._shape,
                  start=start,
                  stop=stop,
                  dtype=self._dtype,
                  container="HDF5IODataset",
              )
  
          dataset = dataset.map(f)
          dataset = dataset.unbatch()
  
          self._dataset = dataset
          super().__init__(self._dataset._variant_tensor)
  
imports: import tensorflow
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 1 to create a new HDF5IODataset object from inherited base classes: tf.data with example: obj = HDF5IODataset(filename, dataset, spec, internal)