const XlaCompilationResult& compilation_result, llvm::raw_ostream& output) {
  const xla::HloModuleConfig module_config(
      compilation_result.computation->GetProgramShape().value());
  auto status_or_hlo_module = xla::HloModule::CreateFromProto(
      compilation_result.computation->proto(), module_config);
  if (!status_or_hlo_module.ok()) {
    LOG(ERROR) << "Conversion to HLO module failed: "

        } catch (final FileUploadException e) {
            handleFileUploadException(e);
        }
    }

    protected ModuleConfig getModuleConfig(final HttpServletRequest request) {
        return (ModuleConfig) request.getAttribute(LastaWebKey.MODULE_CONFIG_KEY);
    }

    // ===================================================================================

            {
                "type_": type_,
                "class_validators": class_validators,
                "default": default,
                "required": required,
                "model_config": model_config,
                "alias": alias,
            }
        )
    try:
        return ModelField(**kwargs)  # type: ignore[arg-type]
    except (RuntimeError, PydanticSchemaGenerationError):

class RoleEnum(Enum):
    admin = "admin"
    normal = "normal"


class ModelWithConfig(BaseModel):
    role: Optional[RoleEnum] = None

    if PYDANTIC_V2:
        model_config = {"use_enum_values": True}
    else:

        class Config:
            use_enum_values = True


class ModelWithAlias(BaseModel):
    foo: str = Field(alias="Foo")

        field_info=field_info,
        type_=Union[str, List[int]],
        class_validators={},
        model_config=BaseConfig,
    )
    assert not is_pv1_scalar_field(field)


@needs_pydanticv2
def test_get_model_config():
    # For coverage in Pydantic v2
    class Foo(BaseModel):
        model_config = ConfigDict(from_attributes=True)

    foo = Foo()
    config = _get_model_config(foo)

=== "Pydantic v2"

    ```Python hl_lines="9"
    {!> ../../../docs_src/settings/app03_an/config.py!}
    ```

    !!! tip
        The `model_config` attribute is used just for Pydantic configuration. You can read more at <a href="https://docs.pydantic.dev/latest/usage/model_config/" class="external-link" target="_blank">Pydantic Model Config</a>.

=== "Pydantic v1"

    ```Python hl_lines="9-10"

    sub_description: Optional[str] = None
    tags: List[str] = []
    if PYDANTIC_V2:
        model_config = {"json_schema_serialization_defaults_required": True}


class Item(BaseModel):
    name: str
    description: Optional[str] = None
    sub: Optional[SubItem] = None
    if PYDANTIC_V2:
        model_config = {"json_schema_serialization_defaults_required": True}

=== "Pydantic v2"

    In Pydantic version 2, you would use the attribute `model_config`, that takes a `dict` as described in <a href="https://docs.pydantic.dev/latest/usage/model_config/" class="external-link" target="_blank">Pydantic's docs: Model Config</a>.

!!! info

    ```

추가 정보는 있는 그대로 해당 모델의 **JSON 스키마** 결과에 추가되고, API 문서에서 사용합니다.

=== "Pydantic v2"

    Pydantic 버전 2에서 <a href="https://docs.pydantic.dev/latest/usage/model_config/" class="external-link" target="_blank">Pydantic 공식 문서: Model Config</a>에 나와 있는 것처럼 `dict`를 받는 `model_config` 어트리뷰트를 사용할 것입니다.

    `"json_schema_extra"`를 생성된 JSON 스키마에서 보여주고 싶은 별도의 데이터와 `examples`를 포함하는 `dict`으로 설정할 수 있습니다.

=== "Pydantic v1"