// Reduce over spatial dimensions (NCHW)
  %1 = "tf.Const"() {value = dense<[2, 3]> : tensor<2xi32>} : () -> tensor<2xi32>

  // Transpose input: NHWC -> NCHW
  %2 = "tf.Const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
  %3 = "tf.Transpose"(%arg0, %2) : (tensor<?x224x224x3xf32>, tensor<4xi32>) -> tensor<?x3x224x224xf32>

    %1 = "tf.Const"() {value = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
    %2 = "tf.Const"() {value = dense<[7, 7, 3, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
    %3 = "tf.Const"() {value = dense<[[0, 0], [3, 3], [3, 3], [0, 0]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>
    %4 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

  %14 = "mhlo.broadcast_in_dim"(%1) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<i32>) -> tensor<4xi32>
  %15 = mhlo.compare  LT, %13, %14,  SIGNED : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi1>
  %16 = "mhlo.broadcast_in_dim"(%0) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<i32>) -> tensor<4xi32>
  %17 = mhlo.add %13, %16 : tensor<4xi32>

    %17 = "tf.Mul"(%16, %cst_0) : (tensor<2x2xf32>, tensor<f32>) -> tensor<2x2xf32>
    %18 = "tf.AddV2"(%17, %cst) : (tensor<2x2xf32>, tensor<f32>) -> tensor<2x2xf32>
    %19 = "tf.Floor"(%18) : (tensor<2x2xf32>) -> tensor<2x2xf32>
    %20 = "tf.ClipByValue"(%19, %cst_5, %cst_6) : (tensor<2x2xf32>, tensor<f32>, tensor<f32>) -> tensor<2x2xf32>

func.func @testBitcast_v9(%arg0: tensor<3x2xi32>) -> tensor<3x2xf32> {
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x2xi32>) -> tensor<3x2xf32>
  func.return %0 : tensor<3x2xf32>
}

// -----

func.func @testBitcast_v10(%arg0: tensor<3x2xi32>) -> tensor<3x4xf32> {
  // expected-error @+1 {{output tensor shape shall be equal to input tensor shape}}
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x2xi32>) -> tensor<3x4xf32>

  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %cst_0 = arith.constant dense<4> : tensor<2x2xi32>
  %cst_1 = arith.constant dense<0> : tensor<2x2xi32>
  %cst_2 = arith.constant dense<0> : tensor<4x2xi32>
  %0 = "tf.SpaceToBatchND"(%arg0, %cst, %cst_0) : (tensor<1x128x128x3xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<4x68x68x3xf32>

// `tfl.concatenation`.

func.func @concatenate_float(%arg0: tensor<3x2xf32>, %arg1: tensor<1x2xf32>) -> tensor<4x2xf32> {
  %0 = "stablehlo.concatenate"(%arg0, %arg1) {dimension = 0 : i64} : (tensor<3x2xf32>, tensor<1x2xf32>) -> tensor<4x2xf32>
  return %0 : tensor<4x2xf32>
}
// CHECK-LABEL: concatenate_float
// CHECK-NOT: tfl.concatenation
// CHECK: stablehlo.concatenate

// -----

    %0 = "tf.XlaDynamicUpdateSlice"(%arg0, %arg1, %arg2) : (tensor<3x4xi32>, tensor<2x2xi32>, tensor<2xi32>) -> tensor<3x4xi32>
    func.return %0: tensor<3x4xi32>
  }

  // CHECK-LABEL: @sparse_to_dense
  // CHECK-SAME: (%[[ARG0:.*]]: tensor<3x2xi32>, %[[ARG1:.*]]: tensor<3xf32>, %[[ARG2:.*]]: tensor<f32>)
  func.func @sparse_to_dense(%arg0: tensor<3x2xi32>, %arg1: tensor<3xf32>, %arg2: tensor<f32>) -> tensor<3x3xf32> {

  %18 = "tf.Const"() {value = dense<1> : tensor<4xi32>} : () -> tensor<4xi32>
  %57 = "tf.Const"() {value = dense<0> : tensor<4xi32>} : () -> tensor<4xi32>
  %534 = "tf.StridedSlice"(%arg0, %57, %57, %18) {begin_mask = 11 : i64, device = "", ellipsis_mask = 0 : i64, end_mask = 11 : i64, new_axis_mask = 4 : i64, shrink_axis_mask = 0 : i64} : (tensor<*xf32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) -> tensor<*xf32>
  func.return %534 : tensor<*xf32>

func.func @pack(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>) -> tensor<2x2xi32> {
  // CHECK: mhlo.reshape {{.*}} : (tensor<2xi32>) -> tensor<1x2xi32>
  // CHECK: mhlo.reshape {{.*}} : (tensor<2xi32>) -> tensor<1x2xi32>
  // CHECK: "mhlo.concatenate"({{.*}}) <{dimension = 0 : i64}> : (tensor<1x2xi32>, tensor<1x2xi32>) -> tensor<2x2xi32>

  %0 = "tf.Pack"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2x2xi32>