func.func @conv(tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>, tensor<256x3x32x32xf32>) -> (tensor<256x8x7x16xf32>, tensor<256x16x32x32xf32>, tensor<256x8x6x16xf32>, tensor<256x32x32x16xf32>, tensor<256x32x32x16xf32>) {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>, %arg2: tensor<256x3x32x32xf32>) :
   // OK

  %6 = "tfl.broadcast_to"(%arg1, %4) : (tensor<8x7x6x5x?x3x2x1xf32>, tensor<8xi64>) -> tensor<8x7x6x5x?x3x2x1xf32>
  %7 = "tfl.broadcast_to"(%arg2, %4) : (tensor<?x3x2x1xf32>, tensor<8xi64>) -> tensor<8x7x6x5x?x3x2x1xf32>
  %8 = "tfl.select_v2"(%5, %6, %7) : (tensor<8x7x6x5x?x3x2x1xi1>, tensor<8x7x6x5x?x3x2x1xf32>, tensor<8x7x6x5x?x3x2x1xf32>) -> tensor<8x7x6x5x?x3x2x1xf32>
  func.return %8 : tensor<8x7x6x5x?x3x2x1xf32>
}

// -----

  %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x1x16xf32>
  %1 = stablehlo.constant dense<0.000000e+00> : tensor<f32>
  %2 = stablehlo.constant dense<6.000000e+00> : tensor<f32>
  %3 = 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<?x28x28x1xf32>, tensor<3x3x1x16xf32>) -> tensor<?x28x28x16xf32>

  %0 = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[1, 2, 3]> : tensor<3xi64>, name = "broadcast.0"}> : (tensor<8x1x16xf32>) -> tensor<3x8x8x16xf32>
  func.return %0 : tensor<3x8x8x16xf32>
}

// CHECK-LABEL:   func @broadcast_in_dim_general_case(
// CHECK-SAME:                                        %[[VAL_0:.*]]: tensor<3x1x16xf32>) -> tensor<3x8x8x16xf32> {

  %0 = "tf.Less"(%arg0, %arg1) : (tensor<8x7x6x5x?x3x2x1xf32>, tensor<?x3x2x1xf32>) -> tensor<8x7x6x5x?x3x2x1xi1>
  %1 = "tf.SelectV2"(%0, %arg0, %arg1) : (tensor<8x7x6x5x?x3x2x1xi1>, tensor<8x7x6x5x?x3x2x1xf32>, tensor<?x3x2x1xf32>) -> tensor<8x7x6x5x?x3x2x1xf32>
  func.return %1 : tensor<8x7x6x5x?x3x2x1xf32>

  func.return %1 : tensor<256x30x30x16xf32>

// CHECK-DAG: %[[w:.*]] = arith.constant dense<1.000000e+00> : tensor<3x3x3x16xf32>