%0 = stablehlo.convert %arg0 : (tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xi8>
    return %0 : tensor<1x3x3x4xi8>
  }
// CHECK: @uniform_quantize_1
  func.func private @uniform_dequantize_0(%arg0: tensor<1x3x3x4xi8>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1x1x1x1xi8>) -> tensor<1x3x3x4xf32> {
    %0 = stablehlo.convert %arg0 : (tensor<1x3x3x4xi8>) -> tensor<1x3x3x4xf32>

    %11 = "tf.Cast"(%10) {device = ""} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xi8>
    %12 = "tf.Identity"(%11) {device = ""} : (tensor<2x3x4x5xi8>) -> tensor<2x3x4x5xi8>
    %13 = "tf.Cast"(%12) {Truncate = false, device = ""} : (tensor<2x3x4x5xi8>) -> tensor<2x3x4x5xi32>
    %14 = "tf.Sub"(%13, %cst_5) {device = ""} : (tensor<2x3x4x5xi32>, tensor<i32>) -> tensor<2x3x4x5xi32>

func.func @convolution_upstream_same_padding_srq(%arg0: tensor<1x3x3x4x!quant.uniform<i8:f32, 3.000000e+0:-100>>) -> tensor<1x3x3x2x!quant.uniform<i8:f32, 4.000000e+0>> {
  %0 = stablehlo.constant() {value = dense<3> : tensor<3x3x4x2xi8>} : () -> tensor<3x3x4x2x!quant.uniform<i8:f32:3, {2.000000e+2, 3.000000e+3}>>

// 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>,...

//===----------------------------------------------------------------------===//

// CHECK-LABEL: fused_conv2d
func.func @fused_conv2d(%input: tensor<1x300x300x40xi8>,
                        %filter: tensor<3x3x40x40xi8>,
                        %bias: tensor<40xf32>,
                        %act: tensor<0xi8>) -> tensor<1x300x300x40xi8> {

  // CHECK:       %[[v0:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>

// 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]]