// 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>

  %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>
}

// -----

^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x4xf32>) :
  %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {device = "", name = "MobilenetV2/expanded_conv/depthwise/depthwise", T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x30x30x12xf32>

  // 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>