}

// -----

func.func @testBiasAddNoDataFormatOk(tensor<1x32x32x16xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32> {
^bb0(%arg0: tensor<1x32x32x16xf32>, %arg1: tensor<16xf32>):
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT"}: (tensor<1x32x32x16xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
  func.return %0 : tensor<1x32x32x16xf32>
}

// -----

    "tfl.yield"(%4, %5, %6, %7) : (tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>) -> ()
  }) : (tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>) -> (tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>)

  func.func @conv2d_unranked_filter_and_dynamic_batch(%arg0: tensor<?x32x32x3xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
    // CHECK: "tf.Conv2D"
    // CHECK-SAME: -> tensor<?x?x?x?xf32>
    %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<?x32x32x3xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>
  }

    ```

    will be replaced by `_xla_compile_device_type`, with its value set to the value of `device`.

    ```mlir