// CHECK: "tf.Conv2D"(%[[INPUT_TRANSPOSE:[0-9]*]], %arg1)
  // CHECK-SAME: data_format = "NCHW"
  %0 = "tf.Conv2D"(%input, %filter)
       {
         data_format = "NHWC",
         padding = "VALID",
         strides = [1, 1, 1, 1]
       } : (tensor<1x28x28x64xf32>, tensor<3x3x64x64xf32>)
        -> tensor<1x26x26x64xf32>

  func.return %0 : tensor<1x26x26x64xf32>
}

// CHECK-LABEL: func @transposeConv2D_1x1_f32

  (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>

    %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-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]])

// 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>
  }

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>

          explicit_paddings = [],
          padding = "VALID",
          strides = [1, 1, 2, 2]
        } : (tensor<?x3x230x230xf32>, tensor<7x7x3x64xf32>) -> tensor<?x64x112x112xf32>

  // CHECK: %[[CONV0:[0-9]*]] = "tf.Conv2D"
  // CHECK-SAME: %[[PAD]]
  // CHECK-SAME: data_format = "NHWC"
  // CHECK-SAME: strides = [1, 2, 2, 1]

  %6, %batch_mean, %batch_variance, %reserved_1, %reserved_2, %reserved_3 =

  %2 = "tf.Conv2D"(%0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32>
  %3 = "tf.Conv2D"(%1, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32>