%4 = "tf.Cast"(%3) {Truncate = false} : (tensor<1x3x4x3xi32>) -> tensor<1x3x4x3xi8>
    return %4 : tensor<1x3x4x3xi8>
  }
  func.func private @dequantize_i8(%arg0: tensor<1x2x2x3xi8>, %arg1: tensor<f32>, %arg2: tensor<i32>) -> tensor<1x2x2x3xf32> {
    %0 = "tf.Cast"(%arg0) : (tensor<1x2x2x3xi8>) -> tensor<1x2x2x3xi32>
    %1 = "tf.Sub"(%0, %arg2) : (tensor<1x2x2x3xi32>, tensor<i32>) -> tensor<1x2x2x3xi32>

// CHECK-NEXT: %[[V2:.*]] = "tf.PartitionedCall"(%[[V1]], %[[V0]], %[[CST_6]], %[[CST]], %[[CST_0]], %[[CST_1]], %[[CST_0]], %[[CST_2]], %[[CST_3]], %[[CST_4]], %[[CST_5]]) <{config = "", config_proto = "", executor_type = "", f = @quantized_conv2d_with_bias_fn_0}> : (tensor<1x3x4x3xi8>, tensor<2x3x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<f32>, tensor<i32>, tensor<f32>,...

    %0 = stablehlo.convert %arg0 : (tensor<1x4x3xf32>) -> tensor<1x4x3xi8>
    return %0 : tensor<1x4x3xi8>
  }
// CHECK: @uniform_quantize_1
  func.func private @uniform_dequantize_0(%arg0: tensor<1x4x3xi8>, %arg1: tensor<1x1x1xf32>, %arg2: tensor<1x1x1xi8>) -> tensor<1x4x3xf32> {
    %0 = stablehlo.convert %arg0 : (tensor<1x4x3xi8>) -> tensor<1x4x3xf32>

// CHECK-SAME: %[[in_scale]], %[[in_zp]], %[[w_scale]], %[[w_zp]],
// CHECK-SAME: %[[b_scale]], %[[w_zp]], %[[out_scale]], %[[out_zp]])
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_fn_0
// CHECK-SAME: (tensor<1x2x2x3xi8>, tensor<2x2x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>, tensor<f32>, tensor<i32>) -> tensor<*xi8>

// CHECK-SAME: (tensor<1x2x2x3xi8>, tensor<2x2x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>, tensor<f32>, tensor<i32>) -> tensor<*xf32>
// CHECK: return %[[conv_quant]]

// CHECK-LABEL: func private @quantized_conv2d_with_bias_and_relu6_float_output_fn_0

// CHECK: %[[minimum:.*]] = "tf.Minimum"(%[[maximum]]
// CHECK: %[[round:.*]] = "tf.Round"(%[[minimum]]
// CHECK: %[[quant:.*]] = "tf.Cast"(%[[round]]) : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xi8>
// CHECK: %[[pad:.*]] = "tf.PadV2"(%[[quant]]
// CHECK: %[[xlaconv:.*]] = "tf.XlaConvV2"(%[[pad]]
// CHECK: %[[sub:.*]] = "tf.Sub"(%[[xlaconv]]

func.func @dot_general_upstream_srq_adj_y(%arg0: tensor<1x2x3x4x!quant.uniform<i8:f32, 1.000000e+0>>) -> tensor<1x2x3x5x!quant.uniform<i8:f32, 4.000000e+0>> {
  %0 = stablehlo.constant() {value = dense<1> : tensor<1x2x5x4xi8>} : () -> tensor<1x2x5x4x!quant.uniform<i8:f32, 1.000000e+0>>
  %1 = "stablehlo.dot_general"(%arg0, %0) {
    dot_dimension_numbers = #stablehlo.dot<
      lhs_batching_dimensions = [0, 1],

  // CHECK-DAG: %[[CONVERT_1:.*]] = mhlo.bitcast_convert %[[LHS]] : (tensor<1x6x6x3x!quant.uniform<i8:f32, 2.000000e+00:4>>) -> tensor<1x6x6x3xi8>
  // CHECK-DAG: %[[CONVERT_2:.*]] = mhlo.bitcast_convert %[[CONVERT_1]] : (tensor<1x6x6x3xi8>) -> tensor<1x6x6x3x!quant.uniform<i8:f32, 2.000000e+00:4>>
  // CHECK: mhlo.convolution(%[[CONVERT_2]], %[[RHS]])

    // CHECK-DAG:  %[[ONE:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
    // CHECK:   %[[PRED:.*]] = "tf.Greater"(%[[FEATURES]], %[[ZERO]]) : (tensor<1x4x4x3xf32>, tensor<f32>) -> tensor<1x4x4x3xi1>
    // CHECK-NEXT:  %[[SCALED_FEATURES:.*]] = "tf.Mul"(%[[FEATURES]], %[[SCALE]]) : (tensor<1x4x4x3xf32>, tensor<f32>) -> tensor<1x4x4x3xf32>

    // CHECK-NEXT: %[[CMP:.*]] = mhlo.compare GT, %[[INP]], %[[ZERO]], NOTYPE : (tensor<1x4x4x3xf32>, tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xi1>
    // CHECK-NEXT: %[[RES:.*]] = mhlo.select %[[CMP]], %[[INP]], %[[LEAKY]] : tensor<1x4x4x3xi1>, tensor<1x4x4x3xf32>
    // CHECK-NEXT: return %[[RES]] : tensor<1x4x4x3xf32>