Doc(
                """
                Custom operation ID to be used by this *path operation*.

                By default, it is generated automatically.

                If you provide a custom operation ID, you need to make sure it is
                unique for the whole API.

                You can customize the
                operation ID generation with the parameter

            Doc(
                """
                Custom operation ID to be used by this *path operation*.

                By default, it is generated automatically.

                If you provide a custom operation ID, you need to make sure it is
                unique for the whole API.

                You can customize the
                operation ID generation with the parameter

def TensorListOpsDecompositionPass : Pass<"tf-tensor-list-ops-decomposition", "ModuleOp"> {
  let summary = "Decomposes TensorList operations into generic operations on tensors.";

  let description = [{
This pass rewrites TensorList operations into generic and non-mutating
operations on tensors. This results in operations that can be legalized to XLA.

The list is converted to a single large tensor that includes all list elements,

  // region that would not be isolated.
  for (int iteration = 0; iteration < max_iterations && changed; ++iteration) {
    changed = false;
    LLVM_DEBUG(llvm::dbgs()
               << "Shape inference, iteration " << iteration << "\n");
    auto res = region->walk([&](Operation* op) {
      auto abstract_op = op->getRegisteredInfo();

				},
			},
		},
		expectedError: `spec.matchConstraints.resourceRules[0].operations: Required value, spec.matchConstraints.resourceRules[1].operations: Required value, spec.matchConstraints.excludeResourceRules[0].operations: Required value, spec.matchConstraints.excludeResourceRules[1].operations: Required value`,
	}, {
		name: "\"\" is NOT a valid operation",
		config: &admissionregistration.ValidatingAdmissionPolicy{

	tests := []struct {
		name       string
		operations []operation
		want       string
	}{
		{
			name: "add pods to activeQ",
			operations: []operation{
				add,
			},
			want: `
            scheduler_queue_incoming_pods_total{event="PodAdd",queue="active"} 3
`,
		},
		{
			name: "add pods to unschedulablePods",
			operations: []operation{
				popAndRequeueAsUnschedulable,
			},

the corresponding iteration value will not be produced. If a map entry is
created during iteration, that entry may be produced during the iteration or
may be skipped. The choice may vary for each entry created and from one
iteration to the next.
If the map is <code>nil</code>, the number of iterations is 0.
</li>

<li>
For channels, the iteration values produced are the successive values sent on

  // When generating the FlatBuffer output some operations have
  // Options (as defined in the schema). These options are effectively
  // the attributes of the operations (e.g., what padding is to be used
  // for a pooling operator). Not all operations have Options and some
  // operations share Options. The following attributes indicate whether
  // the operation has Options in the serialized FlatBuffer.

  auto loc = op.getLoc();

  // First unpack user must be coeff-wise unary operation.
  Operation *first_user = *op->getUsers().begin();
  if (!first_user->hasTrait<OpTrait::TF::CwiseUnary>()) return failure();

  // All unpack users must be defined by the op of same kind.
  bool users_same_op = llvm::all_of(op->getUsers(), [&](Operation *user) {
    return user->getName() == first_user->getName();
  });

   * An object that holds the final result of an asynchronous {@link ClosingFuture} operation and
   * allows the user to close all the closeable objects that were captured during it for later
   * closing.
   *
   * <p>The asynchronous operation will have completed before this object is created.
   *
   * @param <V> the type of the value of a successful operation
   * @see ClosingFuture#finishToValueAndCloser(ValueAndCloserConsumer, Executor)