// RUN: tf-tfrt-opt -tf-to-tfrt="enable-while-parallel-iterations=true" %s | FileCheck %s --dump-input=fail

// CHECK-LABEL: func @cond_false(%arg0: !tfrt.chain, %arg1: !tfrt_fallback.tf_tensor) -> (!tfrt.chain, !tfrt_fallback.tf_tensor)
func.func @cond_false(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<-1> : tensor<i32>} : () -> tensor<i32>

    }

  // Sort matched operations by their position in the block.
  llvm::sort(cluster.operations, [](Operation *a, Operation *b) -> bool {
    return a->isBeforeInBlock(b);
  });

  // Create tf_device::ClusterOp before the last operation in the block that
  // is a part of a match set.
  auto back = cluster.operations.back();
  auto loc = back->getLoc();
  OpBuilder builder(back);

func MergePackageFiles(pkg *Package, mode MergeMode) *File {
	// Count the number of package docs, comments and declarations across
	// all package files. Also, compute sorted list of filenames, so that
	// subsequent iterations can always iterate in the same order.
	ndocs := 0
	ncomments := 0
	ndecls := 0
	filenames := make([]string, len(pkg.Files))
	var minPos, maxPos token.Pos
	i := 0
	for filename, f := range pkg.Files {

	numas := acc.sortAvailableNUMANodes()

	// Calculate the minimum and maximum possible number of NUMA nodes that
	// could satisfy this request. This is used to optimize how many iterations
	// of the loop we need to go through below.
	minNUMAs, maxNUMAs := acc.rangeNUMANodesNeededToSatisfy(cpuGroupSize)

	// Try combinations of 1,2,3,... NUMA nodes until we find a combination

  context:
    cluster: foobar
    user: a name
current-context: default
`,
			wantErr: true,
		},
	}

	for _, tt := range tests {
		// Use a closure so defer statements trigger between loop iterations.
		err := func() error {
			tempfile, err := ioutil.TempFile("", "")
			if err != nil {
				return err
			}
			p := tempfile.Name()
			defer utiltesting.CloseAndRemove(t, tempfile)

	{{break}}
		The innermost {{range pipeline}} loop is ended early, stopping the
		current iteration and bypassing all remaining iterations.

	{{continue}}
		The current iteration of the innermost {{range pipeline}} loop is
		stopped, and the loop starts the next iteration.

	{{template "name"}}
		The template with the specified name is executed with nil data.

	{{template "name" pipeline}}

	// ScalarMult, but in this case the doublings are precomputed. The value
	// [windowValue]G added at iteration k would normally get doubled
	// (totIterations-k)×4 times, but with a larger precomputation we can
	// instead add [2^((totIterations-k)×4)][windowValue]G and avoid the
	// doublings between iterations.
	t := NewP256Point()
	p.Set(NewP256Point())
	tableIndex := len(tables) - 1
	for _, byte := range scalar {

> and you can rely on it after doing initial sanity check and targeted local testing.

### Submitting Your Change

After you submit your pull request, a Gradle developer will review it. It is normal for this to take several iterations, so don't get discouraged by change requests. They ensure the high quality that we all enjoy.

If you need to check on [CI](http://builds.gradle.org/) status as an external contributor, you can click "Log in as guest".

Each test suite has some configuration that is common across for all tests contained in the suite:

  public void testExpectedFpp() {
    BloomFilter<Object> bf = BloomFilter.create(HashTestUtils.BAD_FUNNEL, 10, 0.03);
    double fpp = bf.expectedFpp();
    assertEquals(0.0, fpp);
    // usually completed in less than 200 iterations
    while (fpp != 1.0) {
      boolean changed = bf.put(new Object());
      double newFpp = bf.expectedFpp();
      // if changed, the new fpp is strictly higher, otherwise it is the same