return name


async def get_model_c() -> ModelC:
    return ModelC(username="test-user", password="test-password")


@app.get("/model/{name}", response_model=ModelA)
async def get_model_a(name: str, model_c=Depends(get_model_c)):

        app = FastAPI()

        @app.get("/", responses={"500": {"model": NonPydanticModel}})
        def read_root():
            pass  # pragma: nocover


def test_invalid_response_model_sub_type_in_responses_raises():
    with pytest.raises(FastAPIError):
        app = FastAPI()

        @app.get("/", responses={"500": {"model": List[NonPydanticModel]}})
        def read_root():

# Cookie Parameter Models

If you have a group of **cookies** that are related, you can create a **Pydantic model** to declare them. 🍪

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

/// tip

This same technique applies to `Query`, `Cookie`, and `Header`. 😎

///

import jetbrains.buildServer.configs.kotlin.DslContext
import jetbrains.buildServer.configs.kotlin.buildSteps.GradleBuildStep
import model.CIBuildModel
import model.JsonBasedGradleSubprojectProvider
import model.PerformanceTestCoverage
import model.PerformanceTestType
import model.Stage
import model.StageName
import org.junit.jupiter.api.Assertions.assertEquals
import org.junit.jupiter.api.Test
import java.io.File

		if count := DB.Unscoped().Model(&user).Association(key).Count(); count != value {
			t.Errorf("user's %v expects: %v, got %v", key, value, count)
		}
	}

	for key, value := range map[string]int64{"Account": 0, "Pets": 0, "Toys": 0, "Company": 1, "Manager": 1, "Team": 0, "Languages": 0, "Friends": 0} {
		if count := DB.Model(&user).Association(key).Count(); count != value {

void CompareNumericalAndManualGradients(
    Model model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, int input_index,
    float* expected_grad, int num_grad, bool use_function,
    double abs_error = 1e-2) {
  absl::Status s;
  AbstractTensorHandlePtr numerical_grad;
  {
    AbstractTensorHandle* numerical_grad_raw;
    s = CalcNumericalGrad(ctx, model, inputs, input_index, use_function,

    //
    // p4 inherits from p3
    // p3 inherits from p2
    // p2 inherits from p1
    // p1 inherits from p0
    // p0 inherits from super model
    //
    // or we can show it graphically as:
    //
    // p4 ---> p3 ---> p2 ---> p1 ---> p0 --> super model
    //
    // ----------------------------------------------------------------------

    @Test
    void testProjectInheritance() throws Exception {

 * under the License.
 */
package org.apache.maven.profiles.activation;

import java.util.Properties;

import org.apache.maven.model.Activation;
import org.apache.maven.model.ActivationProperty;
import org.apache.maven.model.Profile;
import org.codehaus.plexus.context.Context;
import org.codehaus.plexus.context.ContextException;

 * limitations under the License.
 */

package gradlebuild.docs.dsl.docbook;

import gradlebuild.docs.dsl.docbook.model.ClassDoc;
import gradlebuild.docs.dsl.docbook.model.ClassExtensionDoc;
import gradlebuild.docs.dsl.docbook.model.MethodDoc;
import org.w3c.dom.Document;
import org.w3c.dom.Element;

import java.util.Collection;

class MethodsRenderer implements ClassDocMemberRenderer {

 * limitations under the License.
 */

package gradlebuild.docs.dsl.docbook;

import gradlebuild.docs.dsl.docbook.model.ClassDoc;
import gradlebuild.docs.dsl.docbook.model.ClassExtensionDoc;
import gradlebuild.docs.dsl.docbook.model.PropertyDoc;
import org.w3c.dom.Document;
import org.w3c.dom.Element;

import java.util.Collection;

class PropertiesRenderer implements ClassDocMemberRenderer {