def METHOD_NAME(job_input_data: dict[str, Any], job_trace: str):
    """Collect k8s info about this job and store it in the OpenSearch record"""
    # Record whether this job was run on a kubernetes pod or via some other
    # means (a UO runner, for example)
    job_input_data["kubernetes_job"] = "Using Kubernetes executor" in job_trace
    # If this job wasn't run on kubernetes, there's no pod to fetch so
    # we can exit early
    if not job_input_data["kubernetes_job"]:
        return
    # Scan job logs to infer the name of the pod this job was executed on
    runner_name_matches = re.findall(
        rf"Running on (.+) via {job_input_data['runner']['description']}...",
        job_trace,
    )
    if not len(runner_name_matches):
        job_input_data["pod_status"] = None
        return
    pod_name = runner_name_matches[0]
    pod: V1Pod | None = None
    while True:
        # Try to fetch pod with kube
        try:
            pod = v1_client.read_namespaced_pod(name=pod_name, namespace="pipeline")
        except ApiException:
            # If it doesn't work, that means the pod has already been cleaned up.
            # In that case, we break out of the loop and return.
            break
        # Check if the pod is still running. If so, keep re-fetching it until it's complete
        status: V1PodStatus = pod.status
        if status.phase != "Running":
            break
        sleep(1)
    if pod:
        job_input_data["pod_status"] = pod.status.to_dict()