code: def TestOneInput(data):
      """Test numeric randomized fuzzing input for tf.raw_ops.Add."""
      fh = FuzzingHelper(data)
  
      # tf.raw_ops.Add also takes tf.bfloat16, tf.half, tf.float32, tf.float64,
      # tf.uint8, tf.int8, tf.int16, tf.int32, tf.int64, tf.complex64,
      # tf.complex128, but get_random_numeric_tensor only generates tf.float16,
      # tf.float32, tf.float64, tf.int32, tf.int64
      input_tensor_x = fh.get_random_numeric_tensor()
      input_tensor_y = fh.get_random_numeric_tensor()
  
      try:
          _ = tf.raw_ops.Add(x=input_tensor_x, y=input_tensor_y)
      except (
          tf.errors.InvalidArgumentError,
          tf.errors.UnimplementedError,
      ):
          pass
  
label_desc: Use image node_id 1 for calling a global function with example usage: TestOneInput(data) without return types