const float ratio_threshold) {
  ElementsAttr attr;
  ShapedType type;
  InspectResult result = {};
  if (auto cst = dyn_cast<ConstOp>(inst)) {
    attr = cst.getValue();
    type = mlir::cast<ShapedType>(cst.getType());
  } else if (auto cst = dyn_cast<QConstOp>(inst)) {
    attr = cst.getValue();
    type = mlir::cast<ShapedType>(cst.getType());
  } else {

      }
    }
  }
  return success();
}

void ModifyIONodesPass::runOnOperation() {
  auto func = getOperation();
  auto attrs = func->getAttrOfType<mlir::DictionaryAttr>("tf.entry_function");

  // Handle the entry functions only.
  if (func.getName() != "main" && (!attrs || attrs.empty())) {
    return;
  }

  OpBuilder builder(func);
  FunctionType func_type = func.getFunctionType();

void go_callback2();

static void *thr(void *arg) {
    go_callback();
    return 0;
}

static void foo() {
    pthread_t th;
    pthread_attr_t attr;
    pthread_attr_init(&attr);
    pthread_attr_setstacksize(&attr, 256 << 10);
    pthread_create(&th, &attr, thr, 0);
    pthread_join(th, 0);
}

static void bar() {
    go_callback2();
}
*/
import "C"

import (
	"log"
	"os"
	"runtime"

    Attribute real_value) {
  if (auto attr = dyn_cast<DenseFPElementsAttr>(real_value)) {
    return convert(attr);
  }
  return nullptr;
}

DenseElementsAttr UniformQuantizedPerAxisValueConverter::convert(
    DenseFPElementsAttr attr) {
  // Creates the converter for each chunk. Normally the size of the
  // quantization dim is 3, so we can cache all the converters.
  ShapedType type = attr.getType();

	Kill      Signal = syscall.Note("kill")
)

func startProcess(name string, argv []string, attr *ProcAttr) (p *Process, err error) {
	sysattr := &syscall.ProcAttr{
		Dir: attr.Dir,
		Env: attr.Env,
		Sys: attr.Sys,
	}

	sysattr.Files = make([]uintptr, 0, len(attr.Files))
	for _, f := range attr.Files {
		sysattr.Files = append(sysattr.Files, f.Fd())
	}

      elements_out(i) = elements_in(i);
    }
  } else {
    AllocatorAttributes attr;
    attr.set_gpu_compatible(true);
    attr.set_nic_compatible(true);
    TF_RETURN_IF_ERROR(context->allocate_temp(
        var->tensor()->dtype(), var->tensor()->shape(), &tmp, attr));
    tensorflow::Status s;
    TF_Tensor* tf_tmp = TF_TensorFromTensor(tmp, &s);
    TF_Tensor* tf_tensor = TF_TensorFromTensor(*var->tensor(), &s);

  f_name_attr.set_name(fd.signature().name());
  TF_ASSERT_OK(
      NodeBuilder("B", "StatefulPartitionedCall", &root.graph()->flib_def())
          .Input(inputs)
          .Attr("Tin", {DT_RESOURCE})
          .Attr("Tout", {DT_RESOURCE})
          .Attr("f", f_name_attr)
          .Finalize(root.graph(), &call));
  call->AddAttr(std::string(kMustCompileAttr), true);

  TF_ASSERT_OK(root.ToGraph(&graph));

    from pydantic.fields import (  # type: ignore[no-redef,attr-defined]
        Required as Required,  # noqa: F401
    )
    from pydantic.fields import (  # type: ignore[no-redef,attr-defined]
        Undefined as Undefined,
    )
    from pydantic.fields import (  # type: ignore[no-redef, attr-defined]
        UndefinedType as UndefinedType,  # noqa: F401
    )
    from pydantic.schema import (
        field_schema,

    "'TFLite_Detection_PostProcess:3' type: DT_FLOAT } attr : { name: "
    "'h_scale' type: 'float'} attr : { name: 'max_classes_per_detection' "
    "type: 'int'} attr : { name: 'max_detections' type: 'int'} attr : { "
    "name: 'nms_iou_threshold' type: 'float'} attr : { name: "
    "'nms_score_threshold' type: 'float'} attr : { name: 'num_classes' type: "
    "'int'} attr : { name: 'w_scale' type: 'float'} attr : { name: 'x_scale' "

    output_tys.push_back(mlir_ty);
  }
  llvm::SmallVector<mlir::NamedAttribute, 4> attrs;
  for (const auto& attr : node_def.attr()) {
    TF_ASSIGN_OR_RETURN(auto mlir_attr,
                        ConvertAttributeValue(attr.second, &builder));
    attrs.push_back({mlir::StringAttr::get(context, attr.first), mlir_attr});
  }

  mlir::Location loc = mlir::UnknownLoc::get(context);