def METHOD_NAME(id: Optional[str] = None,
                     include_job_instructions: Optional[bool] = None,
                     include_label_categories: Optional[bool] = None,
                     resource_group_name: Optional[str] = None,
                     workspace_name: Optional[str] = None,
                     opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetLabelingJobResult:
    """
    Azure Resource Manager resource envelope.
    :param str id: The name and identifier for the LabelingJob.
    :param bool include_job_instructions: Boolean value to indicate whether to include JobInstructions in response.
    :param bool include_label_categories: Boolean value to indicate Whether to include LabelCategories in response.
    :param str resource_group_name: The name of the resource group. The name is case insensitive.
    :param str workspace_name: Name of Azure Machine Learning workspace.
    """
    __args__ = dict()
    __args__['id'] = id
    __args__['includeJobInstructions'] = include_job_instructions
    __args__['includeLabelCategories'] = include_label_categories
    __args__['resourceGroupName'] = resource_group_name
    __args__['workspaceName'] = workspace_name
    opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)
    __ret__ = pulumi.runtime.invoke('azure-native:machinelearningservices/v20230601preview:getLabelingJob', __args__, opts=opts, typ=GetLabelingJobResult).value
    return AwaitableGetLabelingJobResult(
        id=pulumi.get(__ret__, 'id'),
        labeling_job_properties=pulumi.get(__ret__, 'labeling_job_properties'),
        name=pulumi.get(__ret__, 'name'),
        system_data=pulumi.get(__ret__, 'system_data'),
        type=pulumi.get(__ret__, 'type'))