func TestContextCancel(t *testing.T) {
	if runtime.GOOS == "netbsd" && runtime.GOARCH == "arm64" {
		maySkipHelperCommand("cat")
		testenv.SkipFlaky(t, 42061)
	}

	// To reduce noise in the final goroutine dump,
	// let other parallel tests complete if possible.
	t.Parallel()

	ctx, cancel := context.WithCancel(context.Background())
	defer cancel()
	c := helperCommandContext(t, ctx, "cat")

	//
	// Ordering is important here. traceCPUFlush may generate new stacks and dumping
	// stacks may generate new strings.
	traceCPUFlush(gen)
	trace.stackTab[gen%2].dump(gen)
	trace.typeTab[gen%2].dump(gen)
	trace.stringTab[gen%2].reset(gen)

	// That's it. This generation is done producing buffers.
	systemstack(func() {
		lock(&trace.lock)
		trace.flushedGen.Store(gen)
		unlock(&trace.lock)

  }
  mlir::PassManager pm(module.getContext());
  pm.addPass(mlir::createPrintOpGraphPass(output->os()));
  if (failed(pm.run(module))) {
    return errors::Unknown("Failed to dump Op Graph from MLIR module.");
  }
  output->keep();
  return absl::OkStatus();
}

absl::Status ConvertMLIRToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,

package proxy

import (
	"net"
	"reflect"
	"testing"
	"time"

	v1 "k8s.io/api/core/v1"
	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
	"k8s.io/apimachinery/pkg/types"
	"k8s.io/apimachinery/pkg/util/dump"
	"k8s.io/apimachinery/pkg/util/intstr"
	utilfeature "k8s.io/apiserver/pkg/util/feature"
	featuregatetesting "k8s.io/component-base/featuregate/testing"
	"k8s.io/kubernetes/pkg/features"
	netutils "k8s.io/utils/net"
)

  string tf_xla_ops_to_cluster;

  // If non-empty, remove following operations from XLA clustering excludelist.
  string tf_xla_cluster_exclude_ops;

  // Dump graphs during XLA compilation.
  bool tf_xla_clustering_debug;

  // Enables global JIT compilation for CPU via SessionOptions.
  bool tf_xla_cpu_global_jit;

    // both the quantized and unquantized layer.
    DEBUGGER_TYPE_FLOAT_PER_LAYER = 3;
  }

  DebuggerType debugger_type = 1;

  // Path to save unquantized model with dump tensor ops attached.
  // Used when debugger_type is WHOLE_MODEL.
  string unquantized_dump_model_path = 2;

  // Path to save debugger related logs. Defaults to '/tmp/dumps'.
  string log_dir_path = 3;
}

		// (no argument evaluated yet)
		if nargs == 0 {
			check.dump("%v: trace() without arguments", call.Pos())
			x.mode = novalue
			break
		}
		var t operand
		x1 := x
		for _, arg := range argList {
			check.rawExpr(nil, x1, arg, nil, false) // permit trace for types, e.g.: new(trace(T))
			check.dump("%v: %s", x1.Pos(), x1)
			x1 = &t // use incoming x only for first argument
		}

	output := runTestProg(t, "testprog", "BadTraceback")
	for _, want := range []string{
		"unexpected return pc",
		"called from 0xbad",
		"00000bad",    // Smashed LR in hex dump
		"<main.badLR", // Symbolization in hex dump (badLR1 or badLR2)
	} {
		if !strings.Contains(output, want) {
			t.Errorf("output does not contain %q:\n%s", want, output)
		}
	}
}

func TestTimePprof(t *testing.T) {

    return;
  }

  prefix += "/" + file_prefix;
  if (!tensorflow::Env::Default()->CreateUniqueFileName(&prefix, ".mlir")) {
    LOG(WARNING) << "cannot create unique filename, won't dump MLIR module.";
    return;
  }

  std::unique_ptr<WritableFile> file_writer;
  status = env->NewWritableFile(prefix, &file_writer);
  if (!status.ok()) {

		defer func() {
			assert(inferred == nil || len(inferred) == len(tparams) && !containsNil(inferred))
		}()
	}

	if traceInference {
		check.dump("== infer : %s%s ➞ %s", tparams, params, targs) // aligned with rename print below
		defer func() {
			check.dump("=> %s ➞ %s\n", tparams, inferred)
		}()
	}

	// There must be at least one type parameter, and no more type arguments than type parameters.
	n := len(tparams)