operation_security_dict: dict[str, list[str]] = {}
    for security_dependency in flat_dependant._security_dependencies:
        security_definition = jsonable_encoder(
            security_dependency._security_scheme.model,
            by_alias=True,
            exclude_none=True,
        )
        security_name = security_dependency._security_scheme.scheme_name
        security_definitions[security_name] = security_definition

				t.Fatalf("errors happened when query: %v", err)
			} else {
				CheckUser(t, newUser, user)
			}
		}
	}
}

func TestCreateFromMap(t *testing.T) {
	if err := DB.Model(&User{}).Create(map[string]interface{}{"Name": "create_from_map", "Age": 18}).Error; err != nil {
		t.Fatalf("failed to create data from map, got error: %v", err)
	}

	var result User

```

...這個回應會回傳：

```json
{
  "message": "Hello World"
}
```

當然，你大概能找到比格式化 JSON 更好的方式來利用這個能力。😉

### `orjson` 或回應模型 { #orjson-or-response-model }

如果你追求效能，使用[回應模型](../tutorial/response-model.md) 大概會比使用 `orjson` 回應更好。

有了回應模型，FastAPI 會使用 Pydantic 直接將資料序列化為 JSON，而不需要像其他情況那樣先經過 `jsonable_encoder` 之類的中介步驟。

而且在底層，Pydantic 用來序列化為 JSON 的 Rust 機制和 `orjson` 相同，因此用回應模型已經能獲得最佳效能。

	tx.Logger = currentLogger
	return
}

// Pluck queries a single column from a model, returning in the slice dest. E.g.:
//
//	var ages []int64
//	db.Model(&users).Pluck("age", &ages)
func (db *DB) Pluck(column string, dest interface{}) (tx *DB) {
	tx = db.getInstance()
	if tx.Statement.Model != nil {
		if tx.Statement.Parse(tx.Statement.Model) == nil {
			if f := tx.Statement.Schema.LookUpField(column); f != nil {

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

    @Config(defaultValue = "HARD")
    public static final String MAVEN_MODEL_PROCESSOR_REFERENCE_TYPE = "maven.model.processor.referenceType";

    /**
     * User property key prefix for configuring per-object-type reference types.
     * Format: maven.model.processor.referenceType.{ClassName} = {ReferenceType}
     * Example: maven.model.processor.referenceType.Dependency = SOFT
     *
     * @since 4.1.0
     */

    /**
     * Set a given project property. Properties set through this method are only valid
     * for the current build session and do not modify the underlying project model.
     *
     * @param project the project to modify
     * @param key they property's key
     * @param value the value or {@code null} to unset the property
     */

/// check | Inspired **FastAPI** to

Define extra validations for data types using the "default" value of model attributes. This improves editor support, and it was not available in Pydantic before.

`hashed_password`. To fix this, the declared `response_model` is cloned, if it is a Pydantic model class (or contains Pydantic model classes in it, e.g. in a `List[Item]`), the Pydantic model class(es) will be a different one (the "cloned" one). So, an object that is a subclass won't simply pass the validation and returned as-is, because it is no longer a sub-class of the cloned `response_model`. Instead, a new Pydantic model object will be created with the contents of the returned object. So, it will be a...

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