// CHECK: return %[[output]] : tensor<6x3xi8>
  %0 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3xf32>, tensor<3x3xf32>, tensor<i64>) -> tensor<6x3xf32>
  %1 = "tf.UniformQuantize"(%0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<6x3xf32>, tensor<f32>, tensor<i32>) -> tensor<6x3x!tf_type.qint8>
  func.return %1 : tensor<6x3x!tf_type.qint8>
}

func.func @dynamic_update_slice(%arg0: tensor<4x5xi32>, %arg1: tensor<1x5xi32>, %arg2: tensor<2xi32>) -> tensor<4x5xi32> {
  %0 = "tf.XlaDynamicUpdateSlice"(%arg0, %arg1, %arg2) : (tensor<4x5xi32>, tensor<1x5xi32>, tensor<2xi32>) -> tensor<4x5xi32>
  func.return %0 : tensor<4x5xi32>

// CHECK-LABEL:dynamic_update_slice
// CHECK: "tfl.dynamic_update_slice"(%arg0, %arg1, %arg2) : (tensor<4x5xi32>, tensor<1x5xi32>, tensor<2xi32>) -> tensor<4x5xi32>
}

For example, if we have the code

```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.func @concat_v2(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
    // CHECK: "mhlo.concatenate"({{.*}}) <{dimension = 0 : i64}> : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<6x3xf32>
    %axis = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64>
    %1 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3xf32>, tensor<3x3xf32>, tensor<i64>) -> tensor<6x3xf32>
    func.return %1 : tensor<6x3xf32>
  }

//     -> tensor<5x2xf32>
//
// is lowered to
//
//   %shape = "tf.Const"() {value = dense<[-1, 2]> : tensor<2xi64>}
//   %inp0 = "tf.Reshape"(%arg0, %shape)
//     : (tensor<2xf32>, tensor<2xi64>) -> tensor<1x2xf32>
//   %inp1 = "tf.Reshape"(%arg1, %shape)
//     : (tensor<2x2x2xf32>, tensor<2xi64>) -> tensor<4x2xf32>
//   %items0 = "tf.Unpack"(%[[INP0]]) {axis = 0 : i64}

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

// -----

  return {};
}

// Convert Pack to Reshape when there is only one operand to be packed.
// For example,
//
//   %0 = tf.Pack(%input) {axis = 0} // %input : tensor<2x3xf32>
//
// can be canonicalized to
//
//   %shape = "tf.Const"() {value = dense<[1, 2, 3]> : tensor<3xi64>}
//   %0 = tf.Reshape(%input, %shape)
struct ConvertPackToReshape : public OpRewritePattern<PackOp> {