return failure();
    }

    // Constructs strides array.
    // For example, [2, 3] -> [1, 2, 3, 1].
    SmallVector<int64_t, num_spatial_dims + 2> strides({1});
    for (const auto v :
         conv_op.getWindowStrides().value().getValues<int64_t>()) {
      strides.emplace_back(v);
    }
    strides.emplace_back(1);

    // Constructs dilation array.

def LiftConv : Pat<
  (TF_Conv2DOp:$res $input, $filter, $strides, $use_cudnn_on_gpu, $padding,
    $explicit_paddings, IsDataFormatNHWC:$data_format, $dilations),
  (LiftAsTFPartitionedCall<"composite_conv2d_fn">
    (ArgumentList $input, $filter),
    (ResultList $res),
    (NamedAttributeList
      (NamedAttr<"strides"> $strides),
      (NamedAttr<"use_cudnn_on_gpu"> $use_cudnn_on_gpu),

def LiftConv : Pat<
  (TF_Conv2DOp:$res $input, $filter, $strides, $use_cudnn_on_gpu, $padding,
    $explicit_paddings, IsDataFormatNHWC:$data_format, $dilations),
  (LiftAsTFPartitionedCall<"composite_conv2d_fn">
    (ArgumentList $input, $filter),
    (ResultList $res),
    (NamedAttributeList
      (NamedAttr<"strides"> $strides),
      (NamedAttr<"use_cudnn_on_gpu"> $use_cudnn_on_gpu),

  // CHECK: %[[VAL_1:.*]] = "tf.Identity"(%[[VAL_0]]) : (tensor<*xf32>) -> tensor<*xf32>

  (TF_Conv2DOp:$conv
    (TF_SubOp (TF_CastOp $input, $truncate), $input_zp),
    (TF_CastOp (TF_IdentityOp $filter), $truncate1),
    $strides, $use_cudnn, $padding, $explicit_padding,
    IsDataFormatNHWC:$data_format, $dilations),
  (CreateXLAConvOpFromTFConv2DOp
    $input, $filter, $input_zp, $conv, $strides,
    $dilations, $padding, $explicit_padding),
  [(IsInt8ElementType $input),
   (IsInt8ElementType $filter),

func.func @conv2d_backprop_input_with_add(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> {
  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>
  %1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<1xf32>} : () -> tensor<1xf32>

// CHECK-NEXT: %[[CONV2D_0:.*]] = "tf.Conv2D"(%arg0, %arg1)
// CHECK-SAME: data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true
// CHECK-SAME: attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations"
// CHECK-NEXT: %[[BIASADD_0:.*]] = "tf.BiasAdd"(%[[CONV2D_0]], %arg2)
// CHECK-NEXT: %[[RELU6_0:.*]] = "tf.Relu6"(%[[BIASADD_0]])

    %0 = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

// CHECK-SAME: attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations"
// CHECK-NEXT: return %[[CONV2D_0]]

// CHECK-LABEL: private @composite_conv2d_fn_1
// CHECK-NEXT: %[[CONV2D_0:.*]] = "tf.Conv2D"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations"

    %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {
      attr_map = "0:strides,1:padding,2:explicit_paddings,3:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1]
    } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }