let summary = "Asynchronously execution of while op for tf_mlrt";
  let description = [{
    cond: The boolean to control whether the first iteration should be executed.
    arguments: The last $invariant_size elements are invariants between iterations.
    results: a list of futures.
    body_fn: input is [predicate_promise, arg0_future, arg0_promise, arg1_future, arg1_promise, ..., invariant_args] and has no returned results.

      max_us = g.second;
    }
  }
  int max_digits = 1;
  while (max_us >= 10.0) {
    max_us /= 10.0;
    ++max_digits;
  }
  // Dump stats out.
  printf("Benchmark ran %zu iterations over %lld us\n", count_us,
         static_cast<long long>(stats.total_us));  // NOLINT
  for (const auto& g : groups) {
    printf("  %-*s %*.3f us\n", max_label_size, g.first.c_str(), max_digits + 4,
           g.second);

	VMOV V4.B16, V20.B16
	VMOV V5.B16, V21.B16
	VMOV V6.B16, V22.B16
	VMOV V7.B16, V23.B16
	VMOV V8.B16, V24.B16
	VMOV V9.B16, V25.B16
	VMOV V10.B16, V26.B16
	VMOV V11.B16, V27.B16

	// 4 iterations. Each iteration is 8 quarter-rounds.
	MOVD $4, R0
loop:
	QR(V0, V4, V8, V12)
	QR(V1, V5, V9, V13)
	QR(V2, V6, V10, V14)
	QR(V3, V7, V11, V15)

	QR(V0, V5, V10, V15)
	QR(V1, V6, V11, V12)
	QR(V2, V7, V8, V13)

	g1(m)
	g2(m)
	g3(m)
	g4(m)
	g5(m)
}

// Worst-case scenario.
// There are 10 unknown type parameters. In each iteration of
// constraint type inference we infer one more, from right to left.
// Each iteration looks repeatedly at all 11 type parameters,
// requiring a total of 10*11 = 110 iterations with the current
// implementation. Pathological case.

package main

import (
	"fmt"
	"math"
	"time"
)

// checkLinear asserts that the running time of f(n) is in O(n).
// tries is the initial number of iterations.
func checkLinear(typ string, tries int, f func(n int)) {
	// Depending on the machine and OS, this test might be too fast
	// to measure with accurate enough granularity. On failure,

  OpPassManager pass_manager;
  llvm::StringRef device_type = "XLA_CPU_JIT";
  absl::string_view kInlinePass =
      "inline{default-pipeline=canonicalize "
      "inlining-threshold=4294967295 max-iterations=4 }";

  CreateConvertMlirToXlaHloPipeline(pass_manager, device_type,
                                    /*enable_op_fallback=*/true,
                                    /*custom_legalization_passes*/ {});

  }
};

// Test for resource leaks related to TensorFlow session closing requirements
// when loading and unloading large numbers of SavedModelBundles.
// TODO(sukritiramesh): Increase run iterations and move outside of the test
// suite.
TEST_F(LoaderTest, ResourceLeakTest) {
  SavedModelBundleLite bundle;
  SessionOptions session_options;
  RunOptions run_options;

  const string export_dir =

	// The initial temporary is X15, so zero its memory instead
	// of X15 itself.
	MOVL $0, DX
	MOVQ DX, X13
	MOVQ DX, X14
	MOVOU X14, (15*16)(BX)

	// 4 iterations. Each iteration is 8 quarter-rounds.
	MOVL $4, DX
loop:
	QR(X0, X4, X8, X12, X15)
	MOVOU X4, (4*16)(BX) // save X4
	QR(X1, X5, X9, X13, X15)
	MOVOU (15*16)(BX), X15 // reload X15; temp now X4

string BackPropFrameName(const string& forward_frame_name) {
  return strings::StrCat(forward_frame_name, "_backprop");
}

// Creates a loop that counts the number of iterations performed by the
// while loop associated with `while_ctx`. The returned output yields the
// iteration count.
Status AddForwardLoopCounter(WhileContext* while_ctx, const Scope& scope,
                             Output* count) {
  // Create while loop:
  //   i = 0

        # args, so each arg winds up back in the position where it started, but
        # possibly modified.
        #
        # NB: a `for` loop captures its iteration list before it begins, so
        # changing the positional parameters here affects neither the number of
        # iterations, nor the values presented in `arg`.
        shift                   # remove old arg
        set -- "$@" "$arg"      # push replacement arg
    done
fi