result = e.Operand.analyze(s)
	} else {
		result.combine(e.Operand.analyze(s))
		result.combine(e.ConditionRHS.analyze(s))
	}
	return
}

func (e *ConditionRHS) analyze(s *Select) (result qProp) {
	switch {
	case e.Compare != nil:
		result = e.Compare.Operand.analyze(s)
	case e.Between != nil:
		result.combine(e.Between.Start.analyze(s))
		result.combine(e.Between.End.analyze(s))
	case e.In != nil:

	"k8s.io/apimachinery/pkg/runtime/schema"
	"k8s.io/kube-openapi/pkg/validation/spec"
)

// Combine combines the DefinitionsSchemaResolver with a secondary schema resolver.
// The resulting schema resolver uses the DefinitionsSchemaResolver for a GVK that DefinitionsSchemaResolver knows,
// and the secondary otherwise.
func (d *DefinitionsSchemaResolver) Combine(secondary SchemaResolver) SchemaResolver {

	name      string
	analyzers []Analyzer
}

// Combine multiple analyzers into a single one.
// For input metadata, use the union of the component analyzers
func Combine(name string, analyzers ...Analyzer) CombinedAnalyzer {
	return &InternalCombinedAnalyzer{
		name:      name,
		analyzers: analyzers,
	}
}

func (c *InternalCombinedAnalyzer) RelevantSubset(kinds sets.Set[config.GroupVersionKind]) CombinedAnalyzer {

7. Here the `response_model` is using a type annotation of a list of `Author` dataclasses.

    Again, you can combine `dataclasses` with standard type annotations.

8. Notice that this *path operation function* uses regular `def` instead of `async def`.

    As always, in FastAPI you can combine `def` and `async def` as needed.

		{"VADDPD.Z.Z X0, X1, X2", `Z suffix should be the last; duplicate suffix "Z"`},
		{"VADDPD.SAE.BCST X0, X1, X2", `can't combine rounding/SAE and broadcast`},
		{"VADDPD.BCST.SAE X0, X1, X2", `can't combine rounding/SAE and broadcast`},
		{"VADDPD.BCST.Z.SAE X0, X1, X2", `Z suffix should be the last; can't combine rounding/SAE and broadcast`},
		{"VADDPD.SAE.SAE X0, X1, X2", `duplicate suffix "SAE"`},

      options.tpu_transfer_result_to_host;
  Status status = tensorflow::CreateTfExecutorToTfrtPipeline(pm, pass_options);
  if (!status.ok()) {
    return diag_handler.Combine(status);
  }

  if (mlir::failed(pm.run(module)))
    return diag_handler.Combine(tensorflow::errors::Internal(
        "failed to lower TF Dialect to CoreRT dialect."));

  if (VLOG_IS_ON(1)) {
    VLOG(1) << "TFRT dialect: ";

{!../../../docs_src/additional_responses/tutorial003.py!}
```

It will all be combined and included in your OpenAPI, and shown in the API docs:

<img src="/img/tutorial/additional-responses/image01.png">

## Combine predefined responses and custom ones

You might want to have some predefined responses that apply to many *path operations*, but you want to combine them with custom responses needed by each *path operation*.

    String SELF_COMBINATION_MODE_ATTRIBUTE = "combine.self";

    String SELF_COMBINATION_OVERRIDE = "override";

    String SELF_COMBINATION_MERGE = "merge";

    String SELF_COMBINATION_REMOVE = "remove";

    /**
     * In case of complex XML structures, combining can be done based on id.
     */
    String ID_COMBINATION_MODE_ATTRIBUTE = "combine.id";

    /**

// Note that the recomposition map for NFC and NFKC are identical.

// combine returns the combined rune or 0 if it doesn't exist.
//
// The caller is responsible for calling
// recompMapOnce.Do(buildRecompMap) sometime before this is called.
func combine(a, b rune) rune {
	key := uint32(uint16(a))<<16 + uint32(uint16(b))
	if recompMap == nil {

// Note that the recomposition map for NFC and NFKC are identical.

// combine returns the combined rune or 0 if it doesn't exist.
//
// The caller is responsible for calling
// recompMapOnce.Do(buildRecompMap) sometime before this is called.
func combine(a, b rune) rune {
	key := uint32(uint16(a))<<16 + uint32(uint16(b))
	if recompMap == nil {