module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1399 : i32}} {

// CHECK-LABEL: stateless_op
func.func @stateless_op() -> tensor<i32> {
  // CHECK: %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
  %0 = "tf.StatelessRandomGetAlg"() {device = ""} : () -> tensor<i32>
  return %0 : tensor<i32>
}

    %bias_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
    %bias_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
    %output_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
    %output_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>

func.func @while_body(%arg0: tensor<i32> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}) -> (tensor<i32>) {
  %0 = "tf.Const"() { value = dense<1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.AddV2"(%arg0, %0) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %2 = "tf.Const"() { value = dense<16> : tensor<i32> } : () -> tensor<i32>

      %4 = "tfl.sub"(%arg3, %arg4) {fused_activation_function = "NONE"} :
        (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
      "tfl.yield"(%4) : (tensor<*xi32>) -> ()
  }) : (tensor<i32>, tensor<*x!tf_type.resource<tensor<*xi32>>>) -> (tensor<i32>, tensor<*x!tf_type.resource<tensor<*xi32>>>)
    func.return %1#0 : tensor<i32>
  }
}

// -----

// CHECK: module attributes {tfl._legalize_tfl_variables = false}
module {

    %2:2 = tf_executor.island wraps "tf.PlaceholderWithDefault"(%1#0) {type = i32} : (tensor<i32>) -> tensor<*xi32> loc("unranked")
    %3:2 = tf_executor.island wraps "tf.PlaceholderWithDefault"(%1#0) {type = i32} : (tensor<i32>) -> tensor<i32> loc("static")
    %4:2 = tf_executor.island wraps "tf.PlaceholderWithDefault"(%0#0) {type = i32} : (tensor<10xi32>) -> tensor<10xi32> loc("static_10")

  // CHECK: func private @_stablehlo_main_1(%arg0: tensor<i32>) -> tensor<3x11xf32>
  // CHECK: %[[CONSTANT_1:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<3x11xf32>
  // CHECK: return %[[CONSTANT_1:.*]]
  // CHECK: }

  // CHECK: func private @_stablehlo_main_0
  // CHECK-SAME: (%arg0: tensor<i32>, %[[INPUT_1:.*]]: tensor<3x11xf32>)

  %0 = "tf.MyPack"(%arg0) {N=1:i32, axis=0:i32} : (tensor<2x3xf32>) -> tensor<3x2x3xf32>
  func.return %0 : tensor<3x2x3xf32>

// CHECK-NEXT: %[[AXIS:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
// CHECK-NEXT: %[[ED:.*]] = "tf.ExpandDims"(%arg0, %[[AXIS]]) : (tensor<2x3xf32>, tensor<i32>) -> tensor<*xf32>

    // CHECK: name: "foo{{_.*_1}}"
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<1> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo1"
    %2:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<2> : tensor<i32>} : () -> (tensor<i32>) loc("foo1")
    // CHECK: name: "foo{{_.*_2}}"

  // CHECK: _arithmetic_count = 7626752 : i64
  %0 = "tfl.depthwise_conv_2d"(%arg0, %arg1, %arg2) {depth_multiplier = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32, fused_activation_function = "RELU6"} : (tensor<1x112x112x3xf32>, tensor<1x3x3x32xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>
  func.return %0 : tensor<1x112x112x32xf32>
}

  %0 = "tfl.while"(%arg0) ({
  ^bb0(%block: tensor<i32>):
    %1 = "tfl.add"(%arg0, %arg0) { fused_activation_function = "NONE", tac.device = "CPU", tac.inference_type = "FLOAT"} : (tensor<i32>, tensor<i32>) -> (tensor<i32>)
    %2 = "tfl.add"(%1, %1) { fused_activation_function = "NONE", tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<i32>, tensor<i32>) -> (tensor<i32>)
    "tfl.yield"(%2) : (tensor<i32>) -> ()
  },{