from tensorflow.python.ops import gen_nn_ops

    features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
      `int16`, or `int8`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with the same type as `features`.
  """
  with ops.op_scope([features], name, "Relu6") as name:
    features = ops.convert_to_tensor(features, name="features")
    return gen_nn_ops._relu6(features, name=name)


def softmax_cross_entropy_with_logits(logits, labels, name=None):
  """Computes softmax cross entropy between `logits` and `labels`.

  Measures the probability error in discrete classification tasks in which the
  classes are mutually exclusive (each entry is in exactly one class).  For
  example, each CIFAR-10 image is labeled with one and only one label: an image
