readings[0] = l.toArray(String[]::new);

                    if (items == null) {
                        items = new ArrayList<>(text.length() * fields.length / field.length());
                    }

                    final String[] langs = lang == null ? new String[] {} : new String[] { lang };
                    items.add(new SuggestItem(words, readings, new String[] { field }, 1L, 0, -1, tags, roles, langs,

	tx := DB.Session(&gorm.Session{})
	tx.Migrator().DropTable(&ItemContent{}, &Item{})
	tx.AutoMigrate(&ItemContent{}, &Item{})

	item := Item{
		Logo: "logo",
		Contents: []ItemContent{
			{Name: "name", LanguageCode: "en"},
			{Name: "ar name", LanguageCode: "ar"},
		},
	}
	if err := tx.Create(&item).Error; err != nil {
		t.Fatalf("failed to create items, got error: %v", err)
	}

	// test Replace

And Python has a special data type for sets of unique items, the `set`.

Then we can declare `tags` as a set of strings:

{* ../../docs_src/body_nested_models/tutorial003_py310.py hl[12] *}

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

        }
        RenderDataUtil.register(data, "fileConfigItems", itemList);
    }

    /**
     * Creates a dropdown item with label and value.
     *
     * @param label the item label
     * @param value the item value
     * @return map containing the item
     */
    protected Map<String, String> createItem(final String label, final String value) {
        final Map<String, String> map = new HashMap<>(2);

        from fastapi import FastAPI
        from pydantic import BaseModel

        class Item(BaseModel):
            name: str
            description: str | None = None

        app = FastAPI()

        @app.put("/items/{item_id}")
        def replace_item(item_id: str, item: Item):
            return {"message": "Item replaced", "id": item_id}
        ```
        """
        return self.router.put(

    }

    /**
     * Returns the last ExecutionPlanItem in the supplied phase. If no items are in the specified phase,
     * the closest executionPlanItem from an earlier phase item will be returned.
     *
     * @param requestedPhase the requested phase
     *                       The execution plan item
     * @return The ExecutionPlanItem or null if none can be found
     */

from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class Items(BaseModel):
    items: dict[str, int]


@app.post("/foo")
def foo(items: Items):
    return items.items


client = TestClient(app)


def test_additional_properties_post():
    response = client.post("/foo", json={"items": {"foo": 1, "bar": 2}})
    assert response.status_code == 200, response.text

                        },
                        "items": {
                            "title": "Items",
                            "type": "array",
                            "items": {"$ref": "#/components/schemas/Item"},
                        },
                    },
                    "required": ["name", "price", "items"],
                    "title": "Offer",
                    "type": "object",

* look for the subpackage `routers` (the directory at `app/routers/`)...
* and from it, import the submodule `items` (the file at `app/routers/items.py`) and `users` (the file at `app/routers/users.py`)...

The module `items` will have a variable `router` (`items.router`). This is the same one we created in the file `app/routers/items.py`, it's an `APIRouter` object.

And then we do the same for the module `users`.

///

Isso significa: "a variável `items` é uma `list`, e cada um dos itens desta lista é uma `str`".

Ao fazer isso, seu editor pode fornecer suporte mesmo durante o processamento de itens da lista:

<img src="/img/python-types/image05.png">

Sem tipos, isso é quase impossível de alcançar.

Observe que a variável `item` é um dos elementos da lista `items`.