"github.com/spf13/pflag"
)

// Phase provides an implementation of a workflow phase that allows
// creation of new phases by simply instantiating a variable of this type.
type Phase struct {
	// name of the phase.
	// Phase name should be unique among peer phases (phases belonging to
	// the same workflow or phases belonging to the same parent phase).
	Name string

	// Aliases returns the aliases for the phase.
	Aliases []string

A. Unique TensorFlower <******@****.***> 1605121757 -0800

            return Collections.emptyMap();
        }

        Map<String, String> phases = new LinkedHashMap<>();
        for (Map.Entry<String, LifecyclePhase> e : lphases.entrySet()) {
            phases.put(e.getKey(), e.getValue().toString());
        }
        return phases;
    }

    @Deprecated
    public void setPhases(Map<String, String> phases) {
        Map<String, LifecyclePhase> lphases = new LinkedHashMap<>();

		...
	...

Phases are designed to be reusable across different kubeadm workflows thus allowing
e.g. reuse of phase certs in both kubeadm init and kubeadm join --control-plane workflows.

Each workflow can be defined and managed using a Runner, that will run all
the phases according to the given order; nested phases will be executed immediately
after their parent phase.

#########################
## Config for sass-lint
#########################
# Linter Options
options:
  # Don't merge default rules
  merge-default-rules: false
  # Raise an error if more than 50 warnings are generated
  max-warnings: 500
# Rule Configuration
rules:
  attribute-quotes:
    - 2
    -
      include: false
  bem-depth: 2
  border-zero: 2
  brace-style: 2
  class-name-format: 2
  clean-import-paths: 2

// RUN: tf-opt %s -tf-shape-inference=input-arg-shapes=1 -verify-diagnostics -split-input-file | FileCheck %s
// RUN: not tf-opt %s -tf-shape-inference=input-arg-shapes=* 2>&1 | FileCheck --check-prefix=INPUT_ARG_SHAPES_ERROR %s
// INPUT_ARG_SHAPES_ERROR: Missing input argument shapes

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    // CHECK-LABEL: func.func @main

	// initialize the workflow runner with the list of phases
	initRunner.AppendPhase(phases.NewPreflightPhase())
	initRunner.AppendPhase(phases.NewCertsPhase())
	initRunner.AppendPhase(phases.NewKubeConfigPhase())
	initRunner.AppendPhase(phases.NewEtcdPhase())
	initRunner.AppendPhase(phases.NewControlPlanePhase())
	initRunner.AppendPhase(phases.NewKubeletStartPhase())
	initRunner.AppendPhase(phases.NewWaitControlPlanePhase())

                    Map<String, LifecyclePhase> phases = getLifecyclePhases(lifecycleId);
                    if (phases != null) {
                        Lifecycle lifecycle = new Lifecycle();

                        lifecycle.setId(lifecycleId);
                        lifecycle.setLifecyclePhases(phases);

                        lifecycleMap.put(lifecycleId, lifecycle);
                    }

    const ArrayRef<double> scales = qtype.getScales();
    // Broadcasting hasn't been implemented yet.
    if (static_cast<int64_t>(scales.size()) != factor_values.getNumElements())
      return {};
    SmallVector<double, 4> new_scales;
    new_scales.reserve(scales.size());
    auto scales_iter = scales.begin();
    for (const auto& f : factor_values) {

	// initialize the workflow runner with the list of phases
	resetRunner.AppendPhase(phases.NewPreflightPhase())
	resetRunner.AppendPhase(phases.NewRemoveETCDMemberPhase())
	resetRunner.AppendPhase(phases.NewCleanupNodePhase())

	// sets the data builder function, that will be used by the runner
	// both when running the entire workflow or single phases