*   Major new features include `Dataset.from_generator()` (for building an
        input pipeline from a Python generator), and the `Dataset.apply()`
        method for applying custom transformation functions.
    *   Several custom transformation functions have been added, including
        `tf.contrib.data.batch_and_drop_remainder()` and
        `tf.contrib.data.sloppy_interleave()`.

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_ModelDatasetOp : TF_Op<"ModelDataset", [Pure]> {
  let summary = "Identity transformation that models performance.";

  let description = [{
Identity transformation that models performance.
  }];

  let arguments = (ins
    Arg<TF_VariantTensor, [{A variant tensor representing the input dataset.}]>:$input_dataset,