assert jsonable_encoder(pet) == {"name": "Firulais", "owner": {"name": "Foo"}}
    assert jsonable_encoder(pet, include={"name"}) == {"name": "Firulais"}
    assert jsonable_encoder(pet, exclude={"owner"}) == {"name": "Firulais"}
    assert jsonable_encoder(pet, include={}) == {}
    assert jsonable_encoder(pet, exclude={}) == {
        "name": "Firulais",
        "owner": {"name": "Foo"},
    }

    if include is not None and not isinstance(include, (set, dict)):
        include = set(include)
    if exclude is not None and not isinstance(exclude, (set, dict)):
        exclude = set(exclude)
    if isinstance(obj, BaseModel):
        obj_dict = obj.model_dump(
            mode="json",
            include=include,
            exclude=exclude,
            by_alias=by_alias,

                errors=errors,
                body=response_content,
                endpoint_ctx=ctx,
            )

        return field.serialize(
            value,
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )

    else:

    - cron: "0 0 * * 1"

env:
  UV_SYSTEM_PYTHON: 1

jobs:
  test:
    strategy:
      matrix:
        os: [ windows-latest, macos-latest ]
        python-version: [ "3.14" ]
        include:
          - os: ubuntu-latest
            python-version: "3.9"
            coverage: coverage
          - os: macos-latest
            python-version: "3.10"
            coverage: coverage

            """
        ),
    ] = None,
    include_in_schema: Annotated[
        bool,
        Doc(
            """
            To include (or not) this parameter field in the generated OpenAPI.
            You probably don't need it, but it's available.

            This affects the generated OpenAPI (e.g. visible at `/docs`).
            """
        ),

        # self._type_adapter.validate_python(value)
        return self._type_adapter.dump_python(
            value,
            mode=mode,
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )

common:nvcc_clang --config=cuda_nvcc

# Debug config
common:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.
# Users can still include debug info for a specific kernel, e.g. with:

3. Use the following preferred translations when they apply in documentation prose:

- request (HTTP): 請求
- response (HTTP): 回應
- path operation: 路徑操作
- path operation function: 路徑操作函式

The translation can optionally include the original English text only in the first occurrence of each page (e.g. "路徑操作 (path operation)") if the translation is hard to be comprehended by most of the Chinese readers.

### `///` admonitions

        currentUpgradedPropertiesFile.get().asFile,
        baselineUpgradedPropertiesFile.get().asFile,
        {
            it.includedClasses = toPatterns(PublicApi.includes + PublicKotlinDslApi.includes)
            it.excludedClasses = toPatterns(PublicApi.excludes + PublicKotlinDslApi.excludes)

You can combine `dataclasses` with other type annotations in many different combinations to form complex data structures.

Check the in-code annotation tips above to see more specific details.

## Learn More { #learn-more }

You can also combine `dataclasses` with other Pydantic models, inherit from them, include them in your own models, etc.