// CHECK-SAME:      explicit_paddings = [0, 0, 4, 0, 0, 2, 0, 0]
// CHECK-SAME:      (tensor<128x5x4x64xf32>, tensor<3x2x64x4xf32>) -> tensor<128x4x3x4xf32>
// CHECK:           return %[[CONV]] : tensor<128x4x3x4xf32>
// CHECK:         }
func.func @convert_conv2d_negative_explicit_padding(%arg0: tensor<128x7x9x64xf32>, %arg1: tensor<3x2x64x4xf32>) -> tensor<128x4x3x4xf32> {
  %0 = "mhlo.convolution"(%arg0, %arg1) {batch_group_count = 1 : i64,

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>
}

        %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 = 7, cc_minor = 0>}
} {

// CHECK-LABEL: func @transposeConv2D_3x3_f32
func.func @transposeConv2D_3x3_f32(%input: tensor<1x28x28x64xf32>, %filter: tensor<3x3x64x64xf32>) -> tensor<1x26x26x64xf32> {
  // cuDNN prefers NCHW data format for spatial convolutions.
  // CHECK: "tf.Conv2D"(%[[INPUT_TRANSPOSE:[0-9]*]], %arg1)
  // CHECK-SAME: data_format = "NCHW"
  %0 = "tf.Conv2D"(%input, %filter)

    %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-NEXT:    return %2 : tensor<4x?xf32>
}

// - dot_general
//

func.func @convert_dot_general(%arg0: tensor<3x2x6x5x1xf32>, %arg1: tensor<3x2x4x6xf32>) -> tensor<3x5x1x4xf32> {
  %0 = "mhlo.dot_general"(%arg0, %arg1) {
    dot_dimension_numbers = #mhlo.dot<
      lhs_batching_dimensions = [0],
      lhs_contracting_dimensions = [1, 2],