%0 = "tf.UniformQuantizedConvolution"(
    %quant_input, %quant_filter, %input_scale, %input_zp,
    %filter_scale, %filter_zp, %accum_scale, %accum_zp
  ) {
    Tin = "tfdtype$DT_QINT8", Tout = "tfdtype$DT_QINT32",
    attr_map = "", batch_group_count = 1 : i64,
    dimension_numbers = "\10\03\1A\02\01\02 \02(\032\02\00\01@\03J\02\01\02",
    explicit_padding = [], feature_group_count = 1 : i64, lhs_dilation = [1, 1],

  %2 = "tf.XlaCallModule"(%1#0, %0) <{Sout = [#tf_type.shape<1x3>], dim_args_spec = [], disabled_checks = [], has_token_input_output = false, module = "", platforms = [], version = 5 : i64}> {_entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1",...

  // CHECK-SAME: %[[ARG0:[^:[:space:]]+]]
  // CHECK-SAME: %[[ARG1:[^:[:space:]]+]]
  func.func private @serving_default(%arg0: tensor<1x4xf32>, %arg1: tensor<4x3xf32>) -> tensor<1x3xf32> {

#include "pch.h"

void LIB_FUNC Greeter::hello () {
    std::cout << "Hello world!" << std::endl;

#include <iostream>
#include "hello.h"

void LIB_FUNC Greeter::hello () {
    std::cout << "Hello world!" << std::endl;

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 29 : i32}} {
  func.func @main() {
    tf_executor.graph {
      %0 = tf_executor.island wraps "tf.PartitionedCall"() {Tin = [], Tout = [], config = "", config_proto = "", device = "", executor_type = "", f = @foo, name = "Call_foo"} : () -> ()
      tf_executor.fetch
    }
    func.return
  }
  func.func @foo() {
    tf_executor.graph {

    // CHECK: "tf.XlaCallModule"(%[[dq_act]], %[[dq_weight]]
    %1 = "tf.XlaCallModule"(%0, %cst_0, %cst) {
      Sout = [#tf_type.shape<1x2x2x2>], config = "",
      module = "composite_conv2d_with_bias_and_relu6_fn_10",
      _entry_function = @composite_conv2d_with_bias_and_relu6_fn_10,

    %1 = "tf.Identity"(%arg1) {device = ""} : (tensor<100352x10xf32>) -> tensor<100352x10xf32>
    %2 = "tf.XlaCallModule"(%0, %1) <{
        Sout = [#tf_type.shape<?x10>], dim_args_spec = [],
        disabled_checks = [], function_list = [],
        has_token_input_output = false, module = "", platforms = [],
        version = 5 : i64
    }> {

    std::cout << "\tname: '"
              << (output_tensor->name() ? output_tensor->name()->str()
                                        : "<<unnamed>>")
              << "' buffer: " << buffer;
    if (loc) std::cout << llvm::formatv(" {0}", loc(id)).str();
    std::cout << '\n';
  };

  // For every subgraph print out the name (if available), each result's output

	_HW_NCPU       = 3
	_HW_PAGESIZE   = 7
	_HW_NCPUONLINE = 16
)

func sysctlInt(mib []uint32) (int32, bool) {
	var out int32
	nout := unsafe.Sizeof(out)
	ret := sysctl(&mib[0], uint32(len(mib)), (*byte)(unsafe.Pointer(&out)), &nout, nil, 0)
	if ret < 0 {
		return 0, false
	}
	return out, true
}

func getncpu() int32 {
	if n, ok := sysctlInt([]uint32{_CTL_HW, _HW_NCPUONLINE}); ok {
		return int32(n)
	}