def METHOD_NAME(features, default_batch_size=None):
  """Build an input_fn appropriate for serving, expecting feature Tensors.
  Creates an input_fn that expects all features to be fed directly.
  This input_fn is for use at serving time, so the labels return value is always
  None.
  Args:
    features: a dict of string to `Tensor`.
    default_batch_size: the number of query examples expected per batch.
        Leave unset for variable batch size (recommended).
  Returns:
    An input_fn suitable for use in serving.
  """
  def input_fn():
    """an input_fn that expects all features to be fed directly."""
    features_placeholders = {}
    for name, t in features.items():
      shape_list = t.get_shape().as_list()
      shape_list[0] = default_batch_size
      shape = tensor_shape.TensorShape(shape_list)
      features_placeholders[name] = array_ops.placeholder(
          dtype=t.dtype, shape=shape, name=t.op.name)
    labels = None  # these are not known in serving!
    return InputFnOps(features_placeholders, labels, features_placeholders)
  return input_fn