// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG0]], %[[DQ]])
// CHECK{LITERAL}: dim_numbers = [b, 0, 1, f]x[i, 0, 1, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 4 : i64}
// CHECK-SAME: (tensor<1x3x3x4xf32>, tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32>

  %result = "tf_device.launch"() ({
    %3 = "tf.Transpose"(%2, %1) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x8xf32>
    tf_device.return %3: tensor<1x4x4x8xf32>
  }) {device = "device"} : () -> tensor<1x4x4x8xf32>

  func.return %result : tensor<1x4x4x8xf32>

  // CHECK-DAG: %[[CONST1:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}>

  func.return %0, %1, %2 : tensor<1x4x4xf32>, tensor<1x4x4xf32>, tensor<14x4x4xf32>
}

// -----

func.func @testSplitVOpWithBadSizeSplitsConstantSum(%arg0: tensor<16x4x4xf32>) -> (tensor<0x4x4xf32>, tensor<16x4x4xf32>) {

// CHECK:  return
}

func.func @reverse_v2(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1xi32>) -> tensor<1x2x3x4xf32> {
  %0 = "tf.ReverseV2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32>
  func.return %0 : tensor<1x2x3x4xf32>

// CHECK-LABEL:reverse_v2
// CHECK:  "tfl.reverse_v2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32>
// CHECK:  return
}

  %15 = "tf.Identity"(%14) {device = ""} : (tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32>
  func.return %15 : tensor<1x2x4x1xf32>
}

// CHECK-LABEL: func @max_unpooling_2d(
// CHECK-SAME:                         %[[VAL_0:.*]]: tensor<1x1x2x1xf32>,
// CHECK-SAME:                         %[[VAL_1:.*]]: tensor<1x1x2x1xi32>) -> tensor<1x2x4x1xf32> attributes {tf._implements = "MaxUnpooling2D"} {