class_docstr: Dataset specification. Used to pass necessary info to
  user-defined tranformation functions.
  
  Args:
      dataset: Instance of :tf_main:`tf.data.Dataset <data/Dataset>`.
      dataset_size (int): Number of data samples.
      decoder: A (list of) data decoder.
      vocab: A (list of) :class:`~texar.tf.data.Vocab` instance.
      embeddidng: A (list of) :class:`~texar.tf.data.Embedding` instance.
      **kwargs: Any remaining dataset-specific fields.
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code: def __init__(
      self,
      dataset=None,
      dataset_size=None,
      decoder=None,
      vocab=None,
      embedding=None,
      **kwargs,
  ):
      kwargs["dataset"] = dataset
      kwargs["dataset_size"] = dataset_size
      kwargs["decoder"] = decoder
      kwargs["vocab"] = vocab
      kwargs["embedding"] = embedding
      self.__dict__.update(kwargs)
  
imports: import six
  import tensorflow
  import numpy
  from texar.tf.utils import utils
label_desc: Use image node_id 1 to create a new _DataSpec object from inherited base classes: object with example: obj = _DataSpec(dataset, dataset_size, decoder, vocab, embedding)