%0 = stablehlo.constant() {value = dense<3> : tensor<3x3x4x2xi8>} : () -> tensor<3x3x4x2x!quant.uniform<i8:f32, 3.000000e-01:-5>>
  %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<3x3x4x2x!quant.uniform<i8:f32, 3.000000e-01:-5>>) -> tensor<1x3x3x2xf32>

// CHECK: %0 = "tfl.transpose"(%arg0, %cst) : (tensor<1x1x3x3xf32>, tensor<4xi32>) -> tensor<1x3x3x1xf32>
// CHECK{LITERAL}: %cst_0 = arith.constant dense<[[0, 0], [0, 1], [0, 1], [0, 0]]> : tensor<4x2xi32>
// CHECK: %1 = "tfl.pad"(%0, %cst_0) : (tensor<1x3x3x1xf32>, tensor<4x2xi32>) -> tensor<1x4x4x1xf32>

  // CHECK-LABEL: func @conv2d_unranked_input
  func.func @conv2d_unranked_input(%arg0: tensor<*xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<*xf32> {
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: -> tensor<?x?x?x16xf32>
    %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<*xf32>, tensor<3x3x3x16xf32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>
  }

  func.return %resize: tensor<1x3x3x1xi32>
}

// CHECK-LABEL: func @imag_resize_nearest_dyn_img
func.func @imag_resize_nearest_dyn_img(%arg0: tensor<1x?x?x1xi32>) -> tensor<1x3x3x1xi32> {
  %shape = "tf.Const"() {device = "", value = dense<3> : tensor<2xi32>} : () -> tensor<2xi32>

//
// ```
// %0 = stablehlo.constant dense<3>
// %1 = stablehlo.constant dense<4>
// %2 = stablehlo.constant dense<2>
// %3 = stablehlo.convolution(%%arg0, %%arg1) :
//          (tensor<?x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<?x3x4x2xf32>
// %4 = stablehlo.get_dimension_size %3, dim = 0 :
//          (tensor<?x3x4x2xf32>) -> tensor<i32>
// %5 = stablehlo.reshape %4 :
//          (tensor<i32>) -> tensor<1xi32>

func.func @conv_valid_padding(%arg0: tensor<1x4x5x1xf32>, %arg1: tensor<3x3x1x1xf32>) -> tensor<1x2x3x1xf32> {
  // CHECK: mhlo.convolution(%arg0, %arg1)

  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<1x4x5x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x2x3x1xf32>
  func.return %0 : tensor<1x2x3x1xf32>
}

// -----

// CHECK-LABEL: conv_explicit_paddings

// CHECK:  return
}

func.func @topk_4(%arg0: tensor<1x2x3x4xf32>) -> (tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32>) {
  %0 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32>
  %1:2 = "tf.TopKV2"(%arg0, %0) : (tensor<1x2x3x4xf32>, tensor<i32>) -> (tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32>)
  func.return %1#0, %1#1: tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32>

// CHECK-LABEL: topk_4
// CHECK:  "tfl.topk_v2"(%arg0, %cst)