Builder* builder, OperationState* state, int n,
    std::optional<DictionaryAttr> devices,
    llvm::ArrayRef<std::pair<ValueRange, Type>> replicated_inputs,
    ValueRange packed_inputs, TypeRange replica_output_types) {
  DCHECK_GE(n, 2);
  state->addAttribute("n", builder->getI32IntegerAttr(n));

  if (devices.has_value()) state->addAttribute("devices", devices.value());

  Region* region = state->addRegion();

		expectedResult []kubecontainer.CDIDevice
	}{
		{
			description: "empty CDI devices",
			claimInfo: &ClaimInfo{
				ClaimInfoState: state.ClaimInfoState{
					CDIDevices: map[string][]string{},
				},
			},
		},
		{
			description: "nil CDI devices",
			claimInfo:   &ClaimInfo{},
		},
		{
			description: "valid CDI devices",
			claimInfo: &ClaimInfo{
				ClaimInfoState: state.ClaimInfoState{

				annotations = append(annotations, claimAnnotations...)

				devices := claimInfo.cdiDevicesAsList()
				klog.V(3).InfoS("Add CDI devices", "claim", *claimName, "CDI devices", devices)
				cdiDevices = append(cdiDevices, devices...)

				return nil
			})
			if err != nil {
				return nil, fmt.Errorf("locked cache operation: %w", err)
			}
		}
	}

    int64_t device_id;
    if (!device_attr) {
      device_id = kAnyDevice;
    } else if (devices.find(device_attr) != devices.end()) {
      device_id = devices[device_attr];
    } else {
      device_id = 1 + devices.size();
      devices[device_attr] = device_id;
    }

    auto& alias_analysis = side_effect_analysis.GetAliasAnalysis();

LogicalResult GetTpuDeviceAssignment(
    ClusterOp cluster, ReplicateOp replicate, mlir::ModuleOp module,
    absl::StatusOr<TPUDeviceAssignment>& status_or_tpu_device_assignment) {
  mlir::TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(module, &devices))) return failure();

  uint32_t num_replicas = replicate.getN();

  auto num_cores_per_replica_attr = cluster->getAttrOfType<mlir::IntegerAttr>(

void LayoutAssignmentPass::runOnOperation() {
  func::FuncOp func = getOperation();

  // Get runtime devices information from the closest parent module.
  RuntimeDevices devices;
  if (failed(::tensorflow::GetDevicesFromOp(func->getParentOfType<ModuleOp>(),
                                            &devices)))
    return signalPassFailure();

  // If there is no runtime device information and data format is not explicitly

      location, concat_dim_type, concat_dimension_attr);

  // Correctly set output shapes of concat op output if output shape is
  // statically known. Since the shape of TPUExecute op must be the same
  // across logical devices, we refer to the shape of 0th logical device
  // computation output.
  mlir::Type output_type;
  auto input_type = mlir::cast<mlir::TensorType>(inputs[0].getType());

  if (input_type.hasRank()) {

  llvm::SmallVector<Value, 4> inputs;
  // The result values of the function.
  llvm::SmallVector<Value, 4> results;
  // The devices of the input values. It should have the same size as inputs.
  llvm::SmallVector<std::string, 4> input_devices;
  // The devices of the result values. It should have the same size as results.
  llvm::SmallVector<std::string, 4> result_devices;

                                DenseIntElementsAttr replica_groups,
                                Operation* op) {
  // Use special group_key 0 to represent "all available devices". This
  // shall resolve to a DeviceAssignment that includes all devices intended for
  // replica_groups.
  IntegerAttr group_size = builder.getI32IntegerAttr(replica_groups.size());
  IntegerAttr group_key = builder.getI32IntegerAttr(0);

		t.Run(test.name, func(t *testing.T) {
			mounts, devices, contexts := GetPodVolumeNames(test.pod)
			if !mounts.Equal(test.expectedMounts) {
				t.Errorf("Expected mounts: %q, got %q", sets.List[string](mounts), sets.List[string](test.expectedMounts))
			}
			if !devices.Equal(test.expectedDevices) {
				t.Errorf("Expected devices: %q, got %q", sets.List[string](devices), sets.List[string](test.expectedDevices))
			}