%weight_scales: tensor<f32>, %weight_zps: tensor<i32>,
  %output_scales: tensor<f32>, %output_zps: tensor<i32>) -> () {
  // expected-error @below {{'tf.UniformQuantizedDot' op quantization_axis is -1, scales must have 0 rank.}}
  %1 = "tf.UniformQuantizedDot"(
    %input, %weight,
    %input_scales, %input_zps,
    %weight_scales, %weight_zps,
    %output_scales, %output_zps) {
      lhs_quantization_axis = -1 : i64,

          )
      )
    elif target_opset == quant_opts_pb2.UNIFORM_QUANTIZED:
      self.assertTrue(
          self._contains_op(
              output_graphdef,
              'UniformQuantizedDot',
              node_name='sample/matmul.*',
          )
      )

    new_outputs = converted_model.signatures['serving_default'](
        input_tensor=ops.convert_to_tensor(input_data)
    )