// 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>

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32> {
^bb0(%arg0: tensor<3x2xi32>):
  %0 = "arith.constant"() {name = "Const2", value = dense<10> : tensor<i32>} : () -> tensor<i32>
  %1 = "tfl.add"(%0, %arg0) {fused_activation_function = "NONE", name = "add"} : (tensor<i32>, tensor<3x2xi32>) -> tensor<3x2xi32>
  func.return %1 : tensor<3x2xi32>

// CHECK-NEXT: }

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")
  %1,%control_1 = tfl.control_node controls "tfl.sub" (%arg0, %0) {fused_activation_function = "RELU6"} : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32> loc("sub")
  %2 = "arith.constant" () {value = dense<10> : tensor<i32>} : () -> tensor<i32> loc("SameNameAsOutput")

  // CHECK-NEXT:     [[SCALAR:%.*]] = "tfl.pseudo_const"() <{value = dense<10> : tensor<i32>}> : () -> tensor<i32>
  // CHECK-NEXT:     [[ADD:%.*]] = tfl.add([[SCALAR]], [[SUB]]) {fused_activation_function = "NONE"} : (tensor<i32>, tensor<3x2xi32>) -> tensor<3x2xi32>
  // CHECK-NEXT:     return [[ADD]] : tensor<3x2xi32>

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")

func.func @main(%arg0: tensor<3x2xf32>) -> tensor<3x2xi32> {
  // CHECK-LABEL: @main
  // CHECK: "tfl.bucketize"(%arg0) <{boundaries = [0.000000e+00 : f32, 1.000000e+01 : f32, 1.000000e+02 : f32]}> : (tensor<3x2xf32>) -> tensor<3x2xi32>
  %0 = "tfl.bucketize"(%arg0) {boundaries = [0.0 : f32, 10.0 : f32, 100.0 : f32]} : (tensor<3x2xf32>) -> tensor<3x2xi32>
  func.return %0 : tensor<3x2xi32>

  // CHECK: %[[RES_INT_2:.*]] = mhlo.convert %[[RES_INT_1]] : tensor<3x2xi32>
  %result_int = "tf.Cast"(%result) {Truncate = false} : (tensor<3x2x!tf_type.qint32>) -> tensor<3x2xi32>
  // CHECK: return %[[RES_INT_2]] : tensor<3x2xi32>
  func.return %result_int : tensor<3x2xi32>

} {
  func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32>
    attributes {tf.entry_function = {inputs = "input", outputs = "SameNameAsOutput"}} {
  ^bb0(%arg0: tensor<3x2xi32>):
    %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
    %1 = "tfl.sub" (%arg0, %0) {fused_activation_function = "NONE"} : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
    func.return %1 : tensor<3x2xi32>
  }

  %ch0 = tfrt.new.chain

  %a1 = tfrt_fallback_async.const_dense_tensor dense<[[2, 2], [2, 2]]> : tensor<2x2xi32>
  %a2 = tfrt_fallback_async.const_dense_tensor dense<[[3, 3], [3, 3]]> : tensor<2x2xi32>
  %b = tfrt_fallback_async.const_dense_tensor dense<[[1, 1], [1, 1]]> : tensor<2x2xi32>

  // Two batch_size=2 batches get concatenated.

// CHECK-LABEL: main
func.func @main() -> tensor<3x2xi32> {
  // CHECK: "tfl.pseudo_const"() <{value = dense<0> : tensor<3x2xi32>}> {tfl.is_variable} : () -> tensor<3x2xi32>
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>

module {
  func.func @main(%arg0: tensor<1x2xi32>) -> tensor<1x2x2xi32> {
  %0 = "tfl.custom"(%arg0) {custom_code = "stablehlo.broadcast_in_dim", custom_option = #tfl<const_bytes : "0x62726F6164636173745F64696D656E73696F6E73000201020119010101072C022401">} : (tensor<1x2xi32>) -> tensor<1x2x2xi32>
  func.return %0 : tensor<1x2x2xi32>
  }
}

// CHECK:      module {
// CHECK-NEXT:  func @main(%arg0: tensor<1x2xi32>) -> tensor<1x2x2xi32> {