// expected-error @below {{'tf.UniformQuantizedConvolutionHybrid' op quantization_axis is -1, scales must have 0 rank.}}
  %0 = "tf.UniformQuantizedConvolutionHybrid"(%lhs, %rhs, %rhs_scales, %rhs_zps) {
    window_strides = [1, 2],
    padding = "VALID",
    explicit_padding = [],
    lhs_dilation = [1, 1],
    rhs_dilation = [2, 2],
    batch_group_count = 1 : i64,
    feature_group_count = 1 : i64,

    // CHECK: %[[RESULT:.*]] = "mhlo.select_and_scatter"(%[[INPUT]], %[[GRAD]], %[[ZERO]]) <{
    // CHECK-SAME:  padding = dense<0> : tensor<4x2xi64>, window_dimensions = dense<[1, 2, 2, 1]> : tensor<4xi64>, window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>
    // CHECK-SAME }> ({
    // CHECK: ^bb0(%[[VALUE_A:.*]]: tensor<f32>, %[[VALUE_B:.*]]: tensor<f32>):

        }
      }
    }
    return success();
  };

  if (check(op.getWindowDimensions(), "window_dimensions").failed())
    return failure();

  if (check(op.getWindowStrides(), "window_strides").failed()) return failure();

  if (check(op.getBaseDilations(), "base_dilations").failed()) return failure();

  if (check(op.getWindowDilations(), "window_dilations").failed())
    return failure();

  %2 = "tf.Const"() {value = dense<1> : tensor<2x2xi32>} : () -> tensor<2x2xi32> loc("XlaConv/padding")
  %3 = "tf.Const"() {value = dense<1> : tensor<2xi32>} : () -> tensor<2xi32> loc("XlaConv/window_strides")

Same shape condition as `output_scales`.}]>:$output_zero_points,

    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$window_strides,
    StrAttr:$padding,
    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$explicit_padding,
    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$lhs_dilation,

    if target_opset == quant_opts_pb2.UNIFORM_QUANTIZED:
      self.assertTrue(
          self._contains_op(
              output_graphdef,
              'UniformQuantizedConvolutionHybrid',
              'window_strides',
              strides_attr,
          )
      )
      self.assertFalse(
          self._contains_op(output_graphdef, 'DepthwiseConv2dNative')
      )
      if enable_per_channel_quantization: