%0 = "tf.StatefulPartitionedCall"(%arg0) <{
      config = "", config_proto = "", executor_type = "", f = @some_func
    }> {
      _collective_manager_ids = [], device = ""
    } : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32>
    // CHECK-SKIP: tf.StatefulPartitionedCall
    // CHECK-NOSKIP: call @some_func
    // CHECK-NOSKIP-NOT: tf.StatefulPartitionedCall
    %1 = "tf.PartitionedCall"(%0) <{

    %0 = "tf.StatefulPartitionedCall"(%arg0) <{
      config = "", config_proto = "", executor_type = "", f = @some_func
    }> {
      _collective_manager_ids = [], device = ""
    } : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32>
    func.return %0: tensor<1x2x2x3xf32>
  }

  func.func private @some_func(%arg0: tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32> attributes {tf._noinline = true} {
    return %arg0 : tensor<1x2x2x3xf32>

                apply plugin: 'cpp-library'
            }
"""
        file("src/main/headers/main_lib.h") << """
            extern int some_func();
"""
        file("src/main/cpp/main_lib.cpp") << """
            #include <lib.h>
            #include <main_lib.h>
            
            int some_func() { 
                lib_func();
                return 0; 
            }
"""
        file("src/test/cpp/main.cpp") << """

  // CHECK:  "tf._TfrtGetResource"()
  %cond = "tf.Const"() {device = "/CPU:0", value = dense<true> : tensor<i1>} : () -> tensor<i1>
  // CHECK: tf.If
  %x = "tf.If"(%cond, %handle) {then_branch = @some_func, else_branch = @some_func, is_stateless = false} : (tensor<i1>, tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %r = "tf.AddV2"(%arg, %x) {device = "/CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %r : tensor<i32>

}

}

// -----

module attributes {tf_saved_model.semantics} {

// Test sinks in var handle op that used by control flow ops.

// CHECK-LABEL: func private @some_func
func.func private @some_func(
    %arg: tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32> {
  // CHECK: tf.VarHandleOp
  // CHECK: tf.ReadVariableOp

// RUN:   -split-input-file | FileCheck %s --check-prefix=STATIC-RANGE-PTQ-TO-ALL

// STATIC-RANGE-PTQ-TO-ALL-LABEL: @some_func
func.func @some_func(%arg0: tensor<1x1x167xf32>) -> tensor<1x1x64xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<167x64xf32>

    %1 = stablehlo.uniform_quantize %arg0 : (tensor<1x4xf32>) -> tensor<1x4x!quant.uniform<i8:f32, 6.000000e-03:-128>>
    // CHECK: call @some_func
    // CHECK-SAME: -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>

  // CHECK: %[[ADD:.*]] = "tf.StatefulPartitionedCall"(%[[INPUT]]#1, %[[INPUT]]#2)
  // CHECK-SAME: f = @some_func
  // CHECK: "tf.PartitionedCall"(%[[ADD]], %[[INPUT]]#0)
  // CHECK-SAME: f = @some_other_func
  // CHECK: return

  func.func private @some_func(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<3x3xf32> attributes {tf._noinline = true} {

}

// -----

module {
// CHECK-LABEL: func private @some_func
// CHECK-LABEL: func @func_with_call
func.func private @some_func(%arg0: tensor<100xf32>) -> tensor<100xf32> attributes {tf.api_implements = "lstm_b4e9f0e7-ac55-42bc-8ef2-8496419a608c"}
func.func @func_with_call(%arg0: tensor<100xf32>) -> tensor<100xf32> {
  %0 = func.call @some_func(%arg0) : (tensor<100xf32>) -> tensor<100xf32>
  func.return %0 : tensor<100xf32>

      /*inputs=*/{filename_input_type}, /*results=*/{});
  auto save_func = builder.create<func::FuncOp>(
      NameLoc::get(builder.getStringAttr(kTfQuantSaveFuncName)),
      /*sym_name=*/kTfQuantSaveFuncName, func_type);
  save_func.addEntryBlock();
  save_func.setPrivate();

  return save_func;
}

// Creates a save function that contains the `TF::SaveV2Op` for the variables in