### Memória por Processo

Agora, quando o programa carrega coisas na memória, por exemplo, um modelo de aprendizado de máquina em uma variável, ou o conteúdo de um arquivo grande em uma variável, tudo isso **consome um pouco da memória (RAM)** do servidor.

	ctxType
	ctxStmt
	ctxExpr
	ctxField
	ctxParam
	ctxAssign2 // assignment of a single expression to two variables
	ctxSwitch
	ctxTypeSwitch
	ctxFile
	ctxDecl
	ctxSpec
	ctxDefer
	ctxCall  // any function call other than ctxCall2
	ctxCall2 // function call whose result is assigned to two variables
	ctxSelector
)

// walk walks the AST x, calling visit(f, x, context) for each node.

Escribes Python estándar con tipos así:

```Python
from datetime import date

from pydantic import BaseModel

# Declaras la variable como un str
# y obtienes soporte del editor dentro de la función
def main(user_id: str):
    return user_id


# Un modelo de Pydantic
class User(BaseModel):
    id: int
    name: str
    joined: date

            options.addOption(Option.builder()
                    .longOpt(FORCE_INTERACTIVE)
                    .desc(
                            "Run in interactive mode. Overrides, if applicable, the CI environment variable and --non-interactive/--batch-mode options")
                    .build());
            options.addOption(Option.builder(ALTERNATE_USER_SETTINGS)
                    .longOpt("settings")

```Python
commons: Annotated[dict, Depends(common_parameters)]
```

Mas como estamos utilizando `Annotated`, podemos guardar esse valor `Annotated` em uma variável e utilizá-la em múltiplos locais:

//// tab | Python 3.10+

```Python hl_lines="12  16  21"
{!> ../../docs_src/dependencies/tutorial001_02_an_py310.py!}
```

////

//// tab | Python 3.9+

///

### Step 2: create a `FastAPI` "instance"

{* ../../docs_src/first_steps/tutorial001.py hl[3] *}

Here the `app` variable will be an "instance" of the class `FastAPI`.

This will be the main point of interaction to create all your API.

### Step 3: create a *path operation*

#### Path

Sie schreiben Standard-Python mit Typen:

```Python
from typing import List, Dict
from datetime import date

from pydantic import BaseModel

# Deklarieren Sie eine Variable als ein `str`
# und bekommen Sie Editor-Unterstütung innerhalb der Funktion
def main(user_id: str):
    return user_id


# Ein Pydantic-Modell
class User(BaseModel):
    id: int
    name: str

You write standard Python with types:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


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

    }

    TypeToken<V> valueType() {
      return new TypeToken<V>(getClass()) {};
    }
  }

  // The A and B type parameters are used inside the test to test type variable
  public <A, B> void testEquals() {
    new EqualsTester()
        .addEqualityGroup(
            TypeToken.of(String.class),
            TypeToken.of(String.class),

import "k8s.io/apimachinery/pkg/apis/meta/v1/generated.proto";
import "k8s.io/apimachinery/pkg/runtime/generated.proto";
import "k8s.io/apimachinery/pkg/runtime/schema/generated.proto";

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/admission/v1";

// AdmissionRequest describes the admission.Attributes for the admission request.
message AdmissionRequest {