module {
func.func @main(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>, %arg2: tensor<2xi32>) -> tensor<2xi32> {
  %0 = "tfl.custom"(%arg0, %arg1, %arg2) {custom_code = "stablehlo.clamp", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}
}

// CHECK:       module {

  func.return %1 : tensor<2xi32>
}
}

// CHECK:       module {
// CHECK-NEXT:  func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.custom1"}} {
// CHECK-NEXT:    %0 = stablehlo.add %arg0, %arg0 : tensor<2xi32>
// CHECK-NEXT:    %1 = stablehlo.subtract %0, %arg0 : tensor<2xi32>
// CHECK-NEXT:    return %1 : tensor<2xi32>

module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.custom1"}} {
  %0 = "tfl.custom"(%arg0, %arg0) {custom_code = "stablehlo.maximum", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}
}

// CHECK:       module {

module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.custom1"}} {
  %0 = "tfl.custom"(%arg0, %arg0) {custom_code = "stablehlo.multiply", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}
}

// CHECK:       module {

  return %0 : tensor<1x1600x1x1xf32>
}

//CHECK:func.func private @custom_tf_op(%arg0: tensor<1x320x1x1xf32>, %arg1: tensor<2xi32>) -> tensor<1x1600x1x1xf32> {
//CHECK: %0 = "vhlo.custom_call_v1"(%arg0, %arg1) <{

module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.custom1"}} {
  %0 = "tfl.custom"(%arg0, %arg0) {custom_code = "stablehlo.add", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}
}

// CHECK:       module {

// CHECK-DAG:       %[[VAL_3:.*]] = "tfl.pseudo_const"(){{.*}}dense<2> : tensor<1xi32>
// CHECK-DAG:       %[[VAL_4:.*]] = "tfl.pseudo_const"(){{.*}}dense<[2, 1]> : tensor<2xi32>
// CHECK:           %[[VAL_5:.*]] = "tfl.reshape"(%[[VAL_0]], %[[VAL_2]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>

module {
  func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> {
  %0 = "tfl.custom"(%arg0) {custom_code = "stablehlo.reshape", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
  }
}

// CHECK:       module {
// CHECK-NEXT:    func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> {

#loc3])\0Amodule {\0A  func.func @host_func(%arg0: tensor<3360x?xi32> loc(fused[#loc1, #loc2, #loc3]), %arg1: tensor<3xi32> loc(fused[#loc1, #loc2, #loc3]), %arg2: tensor<i32> loc(fused[#loc1, #loc2, #loc3]), %arg3: tensor<2xi32> loc(fused[#loc1, #loc2, #loc3])) -> (tensor<1x1120x?xi32>, tensor<1x1120x?xi32>, tensor<1120x?xi32>, tensor<2xi32>) {\0A    %0 = \22tf.Reshape\22(%arg0, %arg1) {_xla_outside_compilation = \220\22} : (tensor<3360x?xi32>, tensor<3xi32>) -> tensor<3x1120x?xi32> loc(#loc9)\0A    %1:3 = ...

// CHECK: while_region_op
func.func @while_region_op() {
  %elem_shape = "tf.Const"() {value = dense<[-1, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
  %size = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
  // CHECK: TensorListReserve
  // CHECK: TensorListReserve
  %tl = "tf.TensorListReserve"(%elem_shape, %size) : (tensor<2xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
  %while:1 = "tf.WhileRegion"(%tl) ({