t.Fatalf("Failed to query product, got error: %v", err)
	}

	DB.Model(&result3).Update("Price", 800)
	var result4 Product2
	DB.First(&result4, "name = ?", "Nice2")

	if result4.Price != 600 {
		t.Errorf("Admin product's price should not be changed, expects: %v, got %v", 600, result4.Price)
	}
}

type Product3 struct {
	gorm.Model
	Name  string
	Code  string
	Price int64
	Owner string
}

 -----

Maven 4 API - Immutable Toolchains Model

 This is strictly the immutable model for Maven toolchains in <<<org.apache.maven.api.toolchain>>> package.

 The following are generated from this model:

   * {{{./apidocs/index.html}Java sources}} with <<<Builder>>> inner classes for immutable instances creation.

 See also corresponding {{{../../compat/maven-toolchain-model/index.html}Maven classical toolchains model documentation}}.

	if db5.Error != nil {
		t.Errorf("Should not raise error for join where identical fields in different tables. Error: %s", db5.Error.Error())
	}

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.model.building;

import org.apache.maven.model.DependencyManagement;
import org.apache.maven.model.Model;

/**
 * Describes a tag used by the model builder to access a {@link ModelCache}. This interface basically aggregates a name

    get_flat_models_from_fields,
    get_model_name_map,
    get_schema_from_model_field,
    lenient_issubclass,
)
from fastapi.datastructures import DefaultPlaceholder, _Unset
from fastapi.dependencies.models import Dependant
from fastapi.dependencies.utils import (
    _get_flat_fields_from_params,
    get_flat_dependant,
    get_flat_params,
    get_validation_alias,
)
from fastapi.encoders import jsonable_encoder

</div>

## 建立只有單一模型的應用 { #create-the-app-with-a-single-model }

我們先用單一 SQLModel 模型建立這個應用的最簡版。

接著我們會在下方用多個模型來提升安全性與彈性。🤓

### 建立模型 { #create-models }

匯入 `SQLModel` 並建立一個資料庫模型：

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[1:11] hl[7:11] *}

`Hero` 類別與 Pydantic 模型非常相似（事實上，在底層它就是一個 Pydantic 模型）。

有幾點差異：

* `table=True` 告訴 SQLModel 這是一個「資料表模型」（table model），它應該代表 SQL 資料庫中的一個資料表，而不僅僅是「資料模型」（就像一般的 Pydantic 類別）。

	})

	var count1 int64
	if err := DB.Model(&Company{}).Where("name = ?", "company_count_group_a").Group("name").Count(&count1).Error; err != nil {
		t.Errorf("Count should work, but got err %v", err)
	}
	if count1 != 1 {
		t.Errorf("Count with group should be 1, but got count: %v", count1)
	}

	var count2 int64

import jetbrains.buildServer.configs.kotlin.Project
import model.CIBuildModel
import model.SpecificBuild
import model.Stage

/**
 * Subproject under [StageProject] that groups smoke test build configurations.
 */
class SmokeTestProject(
    model: CIBuildModel,
    stage: Stage,
    smokeBuildTypes: List<OsAwareBaseGradleBuildType>,
) : Project({
        id("${model.projectId}_Stage_${stage.stageName.id}_SmokeTest")

 */
package org.apache.maven.model.building;

import javax.inject.Inject;
import javax.inject.Named;
import javax.inject.Singleton;

import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.io.Reader;
import java.util.Map;

import org.apache.maven.model.Model;
import org.apache.maven.model.io.ModelReader;
import org.apache.maven.model.locator.ModelLocator;

Ensuite, dans notre code, nous analysons directement ce contenu YAML, puis nous utilisons à nouveau le même modèle Pydantic pour valider le contenu YAML :

{* ../../docs_src/path_operation_advanced_configuration/tutorial007_py310.py hl[24:31] *}

/// tip | Astuce

Ici, nous réutilisons le même modèle Pydantic.

Mais de la même manière, nous aurions pu le valider autrement.