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,
			},

  // 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.

}

static bool IsConst(Operation* op) {
  return isa<mlir::func::ConstantOp, mlir::arith::ConstantOp, mlir::TF::ConstOp,
             tfl::ConstOp, tfl::QConstOp, tfl::SparseConstOp,
             tfl::SparseQConstOp, mlir::TFL::NoValueOp,
             mlir::stablehlo::ConstantOp, mlir::vhlo::ConstantOpV1>(op);
}

static bool IsTFResourceOp(Operation* op) {
  for (const auto& operand : op->getOperands()) {

	c.dc = nil
	c.db = nil
	return err
}

// Close returns the connection to the connection pool.
// All operations after a Close will return with [ErrConnDone].
// Close is safe to call concurrently with other operations and will
// block until all other operations finish. It may be useful to first
// cancel any used context and then call close directly after.
func (c *Conn) Close() error {

   *
   * <p>Modifications to the backing set are read through to the returned map. The returned map
   * supports removal operations if the backing set does. Removal operations write through to the
   * backing set. The returned map does not support put operations.
   *
   * <p><b>Warning:</b> If the function rejects {@code null}, caution is required to make sure the

     * read operations anyway:
     *
     * - All (unsynchronized) read operations must first read the "count" field, and should not look
     * at table entries if it is 0.
     *
     * - All (synchronized) write operations should write to the "count" field after structurally
     * changing any bin. The operations must not take any action that could even momentarily cause a