padding = "VALID",
         strides = [1, 1, 1, 1]
       } : (tensor<1x28x28x64xf32>, tensor<3x3x64x64xf32>)
        -> tensor<1x26x26x64xf32>

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

// CHECK-LABEL: func @transposeConv2D_1x1_f32
func.func @transposeConv2D_1x1_f32(%input: tensor<1x64x28x28xf32>, %filter: tensor<1x1x64x64xf32>) -> tensor<1x64x28x28xf32> {
  // 1x1 convolution can be computed as a GEMM in NHWC data format.

func.func @conv_fn(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>
  %1 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32>
  func.return %1: tensor<1x3x3x4xf32>
}

  return %1 : tensor<4x8x64x64xf32>
}
func.func private @XlaCallModule_tfl.resize_nearest_neighbor.impl_1(%arg0: tensor<4x8x32x32xf32>) -> tensor<4x8x64x64xf32> {
  %0 = call @XlaCallModule__resize_1(%arg0) : (tensor<4x8x32x32xf32>) -> tensor<4x8x64x64xf32>
  return %0 : tensor<4x8x64x64xf32>
}

        %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>
        %1 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32>

  tf.devices = {"/device:GPU:0" = #tf_type.gpu_device_metadata<cc_major = 6, cc_minor = 0>}
} {

// CHECK-LABEL: func @transposeConv2D_3x3_f16
func.func @transposeConv2D_3x3_f16(%input: tensor<1x28x28x64xf16>, %filter: tensor<3x3x64x64xf16>) -> tensor<1x26x26x64xf16> {
  // cuDNN prefers NCHW data format for spatial convolutions in f16 before
  // compute capability 7.0 (NVIDIA Tensor Cores).

  // CHECK: "tf.Conv2D"(%[[INPUT_TRANSPOSE:[0-9]*]], %arg1)

    %9 = stablehlo.convert %3 : (tensor<3x3x4x4xi8>) -> tensor<3x3x4x4xf32>
    %10 = stablehlo.convolution(%8, %9) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32>
    %11 = stablehlo.reshape %2 : (tensor<1x1x1x1xi8>) -> tensor<1xi8>

// CHECK-DAG:       %[[VAL_4:.*]] = "tf.Transpose"(%[[VAL_1]], %[[VAL_3]]) : (tensor<3x3x32x64xf32>, tensor<4xi64>) -> tensor<3x3x64x32xf32>
// CHECK:           %[[VAL_5:.*]] = "tf.ReverseV2"(%[[VAL_4]], %[[VAL_2]]) : (tensor<3x3x64x32xf32>, tensor<2xi64>) -> tensor<3x3x64x32xf32>
// CHECK:           %[[VAL_6:.*]] = "tf.Const"() <{value = dense<[8, 8, 8, 64]> : tensor<4xi32>}> : () -> tensor<4xi32>

  func.return %1 : tensor<1x4x64x64xf32>
}

// CHECK-LABEL: @AvoidPadStridedSliceNewAxisMaskOnUnknownShapes
func.func @AvoidPadStridedSliceNewAxisMaskOnUnknownShapes(%arg0: tensor<?x?xf32>) -> tensor<1x?x?x1xf32> {

  // CHECK-LABEL: func @shape_from_const_input
  func.func @shape_from_const_input(%arg0: tensor<3x3x32x64xf32>, %arg1: tensor<200x24x24x64xf32>) -> tensor<?x?x?x?xf32> {
    %0 = "tf.Const"() {value = dense<[200, 26, 26, 32]> : tensor<4xi32>} : () -> tensor<4xi32>
    // CHECK: tf.Conv2DBackpropInput
    // CHECK-SAME: (tensor<4xi32>, tensor<3x3x32x64xf32>, tensor<200x24x24x64xf32>) -> tensor<200x26x26x32xf32>