  def testValuesInVariable(self):
    indices = constant_op.constant([[1]], dtype=dtypes.int64)
    values = variables.Variable([1], trainable=False, dtype=dtypes.float32)
    shape = constant_op.constant([1], dtype=dtypes.int64)

    sp_input = sparse_tensor.SparseTensor(indices, values, shape)
    sp_output = sparse_ops.sparse_add(sp_input, sp_input)

    with test_util.force_cpu():
      self.evaluate(variables.global_variables_initializer())
      output = self.evaluate(sp_output)
      self.assertAllEqual(output.values, [2])
The product receives input or data, but it does
        not validate or incorrectly validates that the input has the
        properties that are required to process the data safely and
        correctly.