writeFile(value.file)
        IvyArtifactNameSerializer.INSTANCE.write(this, value.artifactName)
        // TODO - preserve the artifact id implementation instead of unpacking the component id
        componentIdSerializer.write(this, value.id.componentIdentifier)
        // TODO - preserve the artifact's owner id (or get rid of it as it's not used for transforms)
    }

rootProject.name = "preserve-order"...

	var p A
	_ = P2(p) // conversion must be valid
}

func _[P1 S1, P2 S2]() {
	var p P1
	type A = P2
	_ = A(p) // conversion must be valid
}

func _[P int | string]() {
	var p P
	type A = int
	// preserve target type name A in error messages when using Alias types
	// (test are run with and without Alias types enabled, so we need to
	// keep both A and int in the error message)

  if (type.getElementType().isF32()) {
    tflite::internal::sparsity::FormatConverter<float> format_converter(
        shape, traversal_order, format, b_size, b_map);
    std::vector<float> data;
    data.reserve(type.getNumElements());
    for (const auto val : attr.getValues<float>()) data.push_back(val);
    format_converter.DenseToSparse(data.data());
    sparsity =

}

// RegisterReservePlugin returns a function to register a Reserve Plugin to a given registry.
func RegisterReservePlugin(pluginName string, pluginNewFunc runtime.PluginFactory) RegisterPluginFunc {
	return RegisterPluginAsExtensions(pluginName, pluginNewFunc, "Reserve")
}

// RegisterPermitPlugin returns a function to register a Permit Plugin to a given registry.

// RUN: tf-opt %s -tfl-legalize-tf='preserve-assert-op=true' | FileCheck %s --dump-input=fail

func.func @preserve_assert(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi1> {
  %0 = "tf.LessEqual"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1>
  "tf.Assert"(%0, %arg1) {summarize = 3} : (tensor<1xi1>, tensor<1xi32>) -> ()
  func.return %0 : tensor<1xi1>
  // CHECK-LABEL: preserve_assert
  // CHECK: tfl.less_equal
  // CHECK: Assert

  std::vector<std::unique_ptr<ParallelTensor>> per_device_outputs;
  per_device_outputs.reserve(first_op_output_count);
  for (int i = 0; i < first_op_output_count; ++i) {
    std::vector<TensorHandlePtr> components;
    components.reserve(underlying_devices_.size());
    for (int j = 0; j < underlying_devices_.size(); ++j) {

    return result;
  }
  std::vector<ParallelTensor*> parallel_inputs;
  std::vector<std::unique_ptr<ParallelTensor>> implicitly_broadcast_tensors;
  parallel_inputs.reserve(inputs.size());
  implicitly_broadcast_tensors.reserve(inputs.size());  // not tight
  for (const auto& input : inputs) {
    if (absl::holds_alternative<TFE_TensorHandle*>(input)) {
      if (operation_name == std::string("_EagerConst")) {

         "A resource-type output does not have the corresponding "
         "resource-type input.");
  bool change = false;
  // Copy ID set because function `AddValueUniqueIDMapping` below is not
  // guaranteed to preserve pointer stability (see b/243813657).
  const llvm::SmallSet<int64_t, 8> id_set = operand_it->second;
  assert(operand != result);
  for (int64_t id : id_set)
    change = AddValueUniqueIDMapping(result, id) || change;

    (*f.mutable_attr())[attr.first] = attr.second;
  }

  std::vector<Output> func_inputs;
  std::vector<DataType> input_dtypes;
  const int num_inputs = op.num_inputs();
  func_inputs.reserve(num_inputs + grad_inputs.size());
  input_dtypes.reserve(num_inputs);

  for (int i = 0; i < num_inputs; i++) {
    func_inputs.push_back(op.input(i));
    input_dtypes.push_back(op.input_type(i));
  }