!tf_res = tensor<!tf_type.resource<tensor<f32>>>

// Do not convert control outputs to chains in the presence of an op with
// unknown side effects in the while body.
// This test checks that loop signatures are unchanged and no control output is
// erased from while loop body.

// CHECK-LABEL: func @unknown_resource_op_while_body

_Chain control outputs of while loop body_

This pass converts the control outputs of a while loop body function to data
outputs. Thus, inter iteration control dependencies are transformed to
data dependencies. Since data dependencies can express which particular
operations in the while loop body are dependent on which inputs, it captures
inter iteration parallelism in while loop. Control dependencies on the other

  // Inputs to the while loop and the function as a whole
  TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");
  TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");

  // Outputs of the while loop corresponding to the two inputs above
  // The first one will the function's output
  std::vector<TF_Output> outputs;

  // Add while loop to func_graph_
  {
    // The inputs to the while loop

    // condition and body computations.  Dependency between them can be created
    // if a special Identity node in the following pattern is clustered in.
    // That is, an Identity node in the loop cond computation is used to drive
    // const nodes consumed by the loop body.  If this Identity node goes into
    // the same cluster with nodes from the loop body, extra dependency is

		{"symlink", true, fmt.Sprintf("%s/inode/symlink/pod-link", tmpDir)},
		{"loop", false, fmt.Sprintf("%s/inode/symlink/loop", tmpDir)},
		{"loop", true, fmt.Sprintf("%s/inode/symlink/loop", tmpDir)},
	}

	for _, tt := range tests {
		t.Run(tt.name, func(t *testing.T) {
			b := newDefaultBuilder().
				FilenameParam(false, &FilenameOptions{Recursive: tt.recursive, Filenames: []string{tt.directory}}).

  // The resources captured for While loop fall into two categories:
  // (a) read-only. These reads can be replaced by a hoisted read created
  //        before the WhileOp (similar to if and case).
  // (b) written: since the value is written in the loop (which can only in
  //        loop body, all these will become loop variables. Since all resource
  //        variables are removed from the loop variabled during

type Embed0c Embed0

type Embed0p struct {
	image.Point
}

type Embed0q struct {
	Point
}

type embed struct {
	Q int
}

type Loop struct {
	Loop1 int `json:",omitempty"`
	Loop2 int `json:",omitempty"`
	*Loop
}

// From reflect test:
// The X in S6 and S7 annihilate, but they also block the X in S8.S9.
type S5 struct {
	S6
	S7
	S8
}

type S6 struct {

			if err != nil {
				t.Errorf("error parsing protobuf profile: %v", err)
			}

			// High-numbered loop-i goroutines imply that every lower-numbered
			// loop-i goroutine should be present in the profile too.
			counts := make(map[string]int)
			for _, s := range p.Sample {
				label := s.Label[t.Name()+"-loop-i"]
				if len(label) > 0 {
					counts[label[0]]++
				}
			}

	}
	metrics.SyncProxyRulesLastQueuedTimestamp.SetToCurrentTime()
	proxier.syncRunner.Run()
}

// SyncLoop runs periodic work.  This is expected to run as a goroutine or as the main loop of the app.  It does not return.
func (proxier *Proxier) SyncLoop() {
	// Update healthz timestamp at beginning in case Sync() never succeeds.
	if proxier.healthzServer != nil {

  the same. Note that the condition and body regions can implicitly capture
  loop invariant values directly. In canonical form, iteration variables that
  pass through the loop body unmodified are converted to implicitly captured
  references to their values outside the loop.
  }];

  let arguments = (ins
    Variadic<AnyTensor>:$input,