def testSparseTensorSignaturePlaceholders(self):
   tensor = tf.SparseTensor(values=[1.0, 2.0], indices=[[0, 2], [0, 3]],
                            shape=[5, 5])
   signature = tensor_signature.create_signatures(tensor)
   placeholder = tensor_signature.create_placeholders_from_signatures(
       signature)
   self.assertTrue(isinstance(placeholder, tf.SparseTensor))
   self.assertEqual(placeholder.values.dtype, tensor.values.dtype)

  def testTensorSignaturePlaceholders(self):
    placeholder_a = array_ops.placeholder(
        name='test', shape=[None, 100], dtype=dtypes.int32)
    signatures = tensor_signature.create_signatures(placeholder_a)
    placeholder_out = tensor_signature.create_placeholders_from_signatures(
        signatures)
    self.assertEqual(placeholder_out.dtype, placeholder_a.dtype)
    self.assertTrue(placeholder_out.get_shape().is_compatible_with(
        placeholder_a.get_shape()))
    self.assertTrue(
        tensor_signature.tensors_compatible(placeholder_out, signatures))

    inputs = {'a': placeholder_a}
    signatures = tensor_signature.create_signatures(inputs)
    placeholders_out = tensor_signature.create_placeholders_from_signatures(
        signatures)
    self.assertEqual(placeholders_out['a'].dtype, placeholder_a.dtype)
    self.assertTrue(placeholders_out['a'].get_shape().is_compatible_with(
        placeholder_a.get_shape()))
    self.assertTrue(
        tensor_signature.tensors_compatible(placeholders_out, signatures))

    def _get_predict_ops(self, features):
        """Method that builds model graph and returns prediction ops.

    Expected to be overriden by sub-classes that require custom support.
    This implementation uses `model_fn` passed as parameter to constructor to
    build model.

    Args:
      features: `Tensor` or `dict` of `Tensor` objects.

    Returns:
      predictions: `Tensor` or `dict` of `Tensor` objects.
    """
        targets = tensor_signature.create_placeholders_from_signatures(self._targets_info)
        predictions, _, _ = self._call_model_fn(features, targets, ModeKeys.INFER)
        return predictions

  def _get_predict_ops(self, features):
    """Method that builds model graph and returns prediction ops.

    Expected to be overriden by sub-classes that require custom support.
    This implementation uses `model_fn` passed as parameter to constructor to
    build model.

    Args:
      features: `Tensor` or `dict` of `Tensor` objects.

    Returns:
      `ModelFnOps` object.
    """

    self._set_infer_mode_feature_signature(features)
    labels = tensor_signature.create_placeholders_from_signatures(
        self._labels_info[model_fn_lib.ModeKeys.INFER])
    return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.INFER)

 def testTensorPlaceholderNone(self):
   self.assertEqual(
       None, tensor_signature.create_placeholders_from_signatures(None))