```Python
from datetime import date

from pydantic import BaseModel

# Объявляем параметр user_id с типом `str`
# и получаем поддержку редактора внутри функции
def main(user_id: str):
    return user_id


# Модель Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Это можно использовать так:

```Python

      "tracer": "none"
    },
    "proxy_init": {
      "image": "proxyv2"
    },
    "remotePilotAddress": "",
    "sds": {
      "token": {
        "aud": "istio-ca"
      }
    },
    "sts": {
      "servicePort": 0
    },
    "tag": "latest",
    "variant": ""
  },
  "istio_cni": {
    "chained": true,
    "enabled": false,
    "provider": "default"
  },
  "pilot": {
    "cni": {

      "tracer": "none"
    },
    "proxy_init": {
      "image": "proxyv2"
    },
    "remotePilotAddress": "",
    "sds": {
      "token": {
        "aud": "istio-ca"
      }
    },
    "sts": {
      "servicePort": 0
    },
    "tag": "latest",
    "variant": ""
  },
  "istio_cni": {
    "chained": true,
    "enabled": false,
    "provider": "default"
  },
  "pilot": {
    "cni": {

      "tracer": "none"
    },
    "proxy_init": {
      "image": "proxyv2"
    },
    "remotePilotAddress": "",
    "sds": {
      "token": {
        "aud": "istio-ca"
      }
    },
    "sts": {
      "servicePort": 0
    },
    "tag": "latest",
    "variant": ""
  },
  "istio_cni": {
    "chained": true,
    "enabled": false,
    "provider": "default"
  },
  "pilot": {
    "cni": {

                                const StringRef tensor_name) {
  AssetFileDef asset_file_def{};
  asset_file_def.set_filename(MaybeStripAssetDirectoryPrefix(filename).str());

  tensorflow::TensorInfo tensor_info{};
  tensor_info.set_name(tensor_name.str());
  *asset_file_def.mutable_tensor_info() = tensor_info;

  return asset_file_def;
}

// Returns a list of "tf.entry_function" attribute's "inputs" comma-split

from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}


@app.put("/items/{item_id}")

	}

	patch := &unstructured.Unstructured{}
	if obj, ok := a.patch.(map[string]interface{}); ok {
		patch.Object = obj
	} else if str, ok := a.patch.(string); ok {
		str = FixTabsOrDie(str)
		if err := utilyaml.NewYAMLOrJSONDecoder(strings.NewReader(str), len(str)).Decode(&patch.Object); err != nil {
			return err
		}
	} else {
		return fmt.Errorf("invalid patch type: %T", a.patch)
	}

		}
		// Determine proper function or method name.
		if r := n.Recv; r != nil && len(r.List) == 1 {
			t := r.List[0].Type
			star := ""
			if p, _ := t.(*ast.StarExpr); p != nil {
				t = p.X
				star = "*"
			}
			if p, _ := t.(*ast.Ident); p != nil {
				fname = star + p.Name + "." + fname
			}
		}
		walkBody := true
		if *pkgcfg != "" {
			f.preFunc(n, fname)

}

func checkNodeIPs(expected sets.Set[string], actual []net.IP) error {
	notFound := expected.Clone()
	extra := sets.New[string]()
	for _, ip := range actual {
		str := ip.String()
		if notFound.Has(str) {
			notFound.Delete(str)
		} else {
			extra.Insert(str)
		}
	}
	if len(notFound) != 0 || len(extra) != 0 {
		return fmt.Errorf("not found: %v, extra: %v", notFound.UnsortedList(), extra.UnsortedList())
	}
	return nil
}

  }

  void runOnOperation() override;
};

static void IncrementCounterFor(tensorflow::monitoring::Counter<1>* counter,
                                Operation* op) {
  counter->GetCell(op->getName().getStringRef().str())->IncrementBy(1);
}

bool HasBounds(RankedTensorType type) {
  auto encoding = mlir::dyn_cast_or_null<mlir::mhlo::TypeExtensionsAttr>(
      type.getEncoding());
  return (encoding && !encoding.getBounds().empty());
}