// CHECK:           }) : (tensor<1x?xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<3x1xf32>, tensor<3xf32>, tensor<1x3xf32>, tensor<1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x3xf32>

    ```mlir
      %0 = "tf.Const"() {value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %1 = "tf.Const"() {device = "", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
    ```

    then running this pass with 'default-device=foobar', we get:

    ```mlir

    func.return %0 : tensor<128xf32>
  }
  func.func @_func() -> tensor<128xf32> {
    %0 = "tf.Const"() {value = dense<0.0> : tensor<128xf32>} : () -> tensor<128xf32>
    func.return %0 : tensor<128xf32>
  }
}

// -----
// CHECK-LABEL: func @return_from_host_and_tpu

func.func @select_float(%arg0: tensor<1x3xi1>, %arg1: tensor<1x3xf32>, %arg2: tensor<1x3xf32>) -> tensor<1x3xf32> {
  %0 = "stablehlo.select"(%arg0, %arg1, %arg2) : (tensor<1x3xi1>, tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32>
  return %0 : tensor<1x3xf32>
}
// CHECK-LABEL: select_float
// CHECK-NOT: tfl.select_v2
// CHECK: stablehlo.select

// -----

  %noncon3 = "tf.Square"(%noncon2) : (tensor<2xf32>) -> tensor<2xf32>

  %res1 = "tf.DivNoNan"(%arg0, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  %res2 = "tf.MulNoNan"(%arg0, %noncon2) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  %res3 = "tf.DivNoNan"(%arg0, %noncon3) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>

  func.return %24 : tensor<1x4xf32>
}