slice.SetType(s.Type())
		slice.SetBounded(true)

		ptr1, len1 := backingArrayPtrLen(cheapExpr(slice, &nodes))
		ptr2, len2 := backingArrayPtrLen(l2)

		fn := typecheck.LookupRuntime("slicecopy", ptr1.Type().Elem(), ptr2.Type().Elem())
		ncopy = mkcall1(fn, types.Types[types.TINT], &nodes, ptr1, len1, ptr2, len2, ir.NewInt(base.Pos, elemtype.Size()))
	} else {

    {!> ../../../docs_src/query_params_str_validations/tutorial001_py310.py!}
    ```

🔢 🔢 `q` 🆎 `Union[str, None]` (⚖️ `str | None` 🐍 3️⃣.1️⃣0️⃣), 👈 ⛓ 👈 ⚫️ 🆎 `str` ✋️ 💪 `None`, & 👐, 🔢 💲 `None`, FastAPI 🔜 💭 ⚫️ 🚫 ✔.

!!! note
    FastAPI 🔜 💭 👈 💲 `q` 🚫 ✔ ↩️ 🔢 💲 `= None`.

     `Union` `Union[str, None]` 🔜 ✔ 👆 👨‍🎨 🤝 👆 👍 🐕‍🦺 & 🔍 ❌.

## 🌖 🔬

```

O parâmetro de consulta `q` é do tipo `Union[str, None]`, o que significa que é do tipo `str` mas que também pode ser `None`, e de fato, o valor padrão é `None`, então o FastAPI saberá que não é obrigatório.

!!! note "Observação"
    O FastAPI saberá que o valor de `q` não é obrigatório por causa do valor padrão `= None`.

=== "Python 3.10+"

    ```Python
    q: str | None = None
    ```

=== "Python 3.8+"

    ```Python
    q: Union[str, None] = None
    ```

Вот что мы получим, если обернём это в `Annotated`:

=== "Python 3.10+"

    ```Python
    q: Annotated[str | None] = None
    ```

=== "Python 3.8+"

    ```Python
    q: Annotated[Union[str, None]] = None
    ```

    ```

Der Query-Parameter `q` hat den Typ `Union[str, None]` (oder `str | None` in Python 3.10), was bedeutet, er ist entweder ein `str` oder `None`. Der Defaultwert ist `None`, also weiß FastAPI, der Parameter ist nicht erforderlich.

!!! note "Hinweis"
    FastAPI weiß nur dank des definierten Defaultwertes `=None`, dass der Wert von `q` nicht erforderlich ist

// RUN: tf-mlir-translate -mlir-tf-str-attr-to-mlir %s -mlir-print-debuginfo -mlir-print-local-scope | FileCheck %s

"\0A\0Amodule attributes {tf.versions = {producer = 888 : i32}} {\0A func.func @main(%arg0: tensor<?xi32>) -> tensor<?xi32> {\0A %0 = \22tf.Identity\22(%arg0) : (tensor<?xi32>) -> tensor<?xi32> loc(unknown)\0A return %0 : tensor<?xi32> loc(unknown)\0A } loc(unknown)\0A} loc(unknown)"

// Test simple serialized computation consisting of a function named `main`

// RUN: not tf-mlir-translate -mlir-tf-str-attr-to-mlir %s 2>&1 | FileCheck %s

"builtin.totally @invalid MLIR module {here} <-"

	s := nn.Args
	t := make([]ir.Node, 0, len(s))
	for i := 0; i < len(s); {
		var strs []string
		for i < len(s) && ir.IsConst(s[i], constant.String) {
			strs = append(strs, ir.StringVal(s[i]))
			i++
		}
		if len(strs) > 0 {
			t = append(t, ir.NewString(base.Pos, strings.Join(strs, "")))
		}
		if i < len(s) {
			t = append(t, s[i])
			i++
		}
	}
	nn.Args = t

	s := Collect(testSeq)
	want := testSeqResult
	if !Equal(s, want) {
		t.Errorf("got %v, want %v", s, want)
	}
}

var iterTests = [][]string{
	nil,
	{"a"},
	{"a", "b"},
	{"b", "a"},
	strs[:],
}

func TestValuesAppendSeq(t *testing.T) {
	for _, prefix := range iterTests {
		for _, s := range iterTests {
			got := AppendSeq(prefix, Values(s))
			want := append(prefix, s...)

		ClusterDomain:    clusterDomain,
		ResolverConfig:   resolverConfig,
	}
}

func omitDuplicates(strs []string) []string {
	uniqueStrs := make(map[string]bool)

	var ret []string
	for _, str := range strs {
		if !uniqueStrs[str] {
			ret = append(ret, str)
			uniqueStrs[str] = true
		}
	}
	return ret
}