///

Portanto, quando você cria uma instância da classe `Settings` (nesse caso, o objeto `settings`), o Pydantic lê as variáveis de ambiente sem diferenciar maiúsculas e minúsculas, por isso, uma variável maiúscula `APP_NAME` será usada para o atributo `app_name`.

Depois ele irá converter e validar os dados. Assim, quando você utilizar aquele objeto `settings`, os dados terão o tipo que você declarou (e.g. `items_per_user` será do tipo `int`).

///

Wenn Sie dann eine Instanz dieser `Settings`-Klasse erstellen (in diesem Fall als `settings`-Objekt), liest Pydantic die Umgebungsvariablen ohne Berücksichtigung der Groß- und Kleinschreibung. Eine Variable `APP_NAME` in Großbuchstaben wird also als Attribut `app_name` gelesen.

///

Then, when you create an instance of that `Settings` class (in this case, in the `settings` object), Pydantic will read the environment variables in a case-insensitive way, so, an upper-case variable `APP_NAME` will still be read for the attribute `app_name`.

Next it will convert and validate the data. So, when you use that `settings` object, you will have data of the types you declared (e.g. `items_per_user` will be an `int`).

### Use the `settings`

```

/// tip

🚥 👆 💚 🕳 ⏩ 📁 & 📋, 🚫 ⚙️ 👉 🖼, ⚙️ 🏁 1️⃣ 🔛.

///

⤴️, 🕐❔ 👆 ✍ 👐 👈 `Settings` 🎓 (👉 💼, `settings` 🎚), Pydantic 🔜 ✍ 🌐 🔢 💼-😛 🌌,, ↖-💼 🔢 `APP_NAME` 🔜 ✍ 🔢 `app_name`.

⏭ ⚫️ 🔜 🗜 & ✔ 💽. , 🕐❔ 👆 ⚙️ 👈 `settings` 🎚, 👆 🔜 ✔️ 📊 🆎 👆 📣 (✅ `items_per_user` 🔜 `int`).

### ⚙️ `settings`

⤴️ 👆 💪 ⚙️ 🆕 `settings` 🎚 👆 🈸:

```Python hl_lines="18-20"

然后，它将转换和验证数据。因此，当您使用该 `settings` 对象时，您将获得您声明的类型的数据（例如 `items_per_user` 将为 `int` 类型）。

### 使用 `settings`

然后，您可以在应用程序中使用新的 `settings` 对象：

```Python hl_lines="18-20"
{!../../docs_src/settings/tutorial001.py!}
```

### 运行服务器

接下来，您将运行服务器，并将配置作为环境变量传递。例如，您可以设置一个 `ADMIN_EMAIL` 和 `APP_NAME`，如下所示：

<div class="termy">

}  // namespace

absl::Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }
  registry_.insert({op_name, gradient_function_factory});
  return absl::OkStatus();
}

  // `TF_AttrBuilderCheckCanRunOnDevice()` call(s) on the same `builder`.
  std::set<std::string> attr_names;
};

TF_AttrBuilder* TF_NewAttrBuilder(const char* op_name) {
  return new TF_AttrBuilder(op_name);
}

void TF_DeleteAttrBuilder(TF_AttrBuilder* builder) { delete builder; }

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,

  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,
                       absl::Span<AbstractTensorHandle* const> outputs,
                       GradientFunction* gradient_function,
                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has

    }
    if (raw_device_name) {
      device_name_ = raw_device_name;
    }
    op_type_ = op;
    return absl::OkStatus();
  }
  absl::Status SetOpName(const char* const op_name) override {
    if (op_) {
      return errors::FailedPrecondition(
          "SetOpName called on already built op.");
    }
    if (op_type_.empty()) {
      return errors::FailedPrecondition(

  tsl::Set_TF_Status_from_Status(
      s, unwrap(op)->Reset(op_type,
                           /*raw_device_name=*/nullptr));
}

void TF_AbstractOpSetOpName(TF_AbstractOp* op, const char* const op_name,
                            TF_Status* s) {
  TracingOperation* tracing_op = dyn_cast<TracingOperation>(unwrap(op));
  if (!tracing_op) {
    tsl::Set_TF_Status_from_Status(