code: def get_metaflow_kubernetes_operator():
      try:
          from airflow.contrib.operators.kubernetes_pod_operator import (
              KubernetesPodOperator,
          )
      except ImportError:
          try:
              from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import (
                  KubernetesPodOperator,
              )
          except ImportError as e:
              raise KubernetesProviderNotFound(
                  "This DAG utilizes `KubernetesPodOperator`. "
                  "Install the Airflow Kubernetes provider using "
                  "`%s -m pip install apache-airflow-providers-cncf-kubernetes`"
                  % sys.executable
              )
  
      class MetaflowKubernetesOperator(KubernetesPodOperator):
          """
          ## Why Inherit the `KubernetesPodOperator` class ?
  
          Two key reasons :
  
          1. So that we can override the `execute` method.
          The only change we introduce to the method is to explicitly modify xcom relating to `return_values`.
          We do this so that the `XComArg` object can work with `expand` function.
  
          2. So that we can introduce a keyword argument named `mapper_arr`.
          This keyword argument can help as a dummy argument for the `KubernetesPodOperator.partial().expand` method. Any Airflow Operator can be dynamically mapped to runtime artifacts using `Operator.partial(**kwargs).extend(**mapper_kwargs)` post the introduction of [Dynamic Task Mapping](https://airflow.apache.org/docs/apache-airflow/stable/concepts/dynamic-task-mapping.html).
          The `expand` function takes keyword arguments taken by the operator.
  
          ## Why override the `execute` method  ?
  
          When we dynamically map vanilla Airflow operators with artifacts generated at runtime, we need to pass that information via `XComArg` to a operator's keyword argument in the `expand` [function](https://airflow.apache.org/docs/apache-airflow/stable/concepts/dynamic-task-mapping.html#mapping-over-result-of-classic-operators).
          The `XComArg` object retrieves XCom values for a particular task based on a `key`, the default key being `return_values`.
          Oddly dynamic task mapping [doesn't support XCom values from any other key except](https://github.com/apache/airflow/blob/8a34d25049a060a035d4db4a49cd4a0d0b07fb0b/airflow/models/mappedoperator.py#L150) `return_values`
          The values of XCom passed by the `KubernetesPodOperator` are mapped to the `return_values` XCom key.
  
          The biggest problem this creates is that the values of the Foreach cardinality are stored inside the dictionary of `return_values` and cannot be accessed trivially like : `XComArg(task)['foreach_key']` since they are resolved during runtime.
          This puts us in a bind since the only xcom we can retrieve is the full dictionary and we cannot pass that as the iterable for the mapper tasks.
          Hence, we inherit the `execute` method and push custom xcom keys (needed by downstream tasks such as metaflow taskids) and modify `return_values` captured from the container whenever a foreach related xcom is passed.
          When we encounter a foreach xcom we resolve the cardinality which is passed to an actual list and return that as `return_values`.
          This is later useful in the `Workflow.compile` where the operator's `expand` method is called and we are able to retrieve the xcom value.
          """
  
          template_fields = KubernetesPodOperator.template_fields + (
              "metaflow_pathspec",
              "metaflow_run_id",
              "metaflow_task_id",
              "metaflow_attempt",
              "metaflow_step_name",
              "metaflow_flow_name",
          )
  
          def __init__(
              self,
              *args,
              mapper_arr=None,
              flow_name=None,
              flow_contains_foreach=False,
              **kwargs,
          ) -> None:
              super().__init__(*args, **kwargs)
              self.mapper_arr = mapper_arr
              self._flow_name = flow_name
              self._flow_contains_foreach = flow_contains_foreach
              self.metaflow_pathspec = AIRFLOW_MACROS.pathspec(
                  self._flow_name,
                  is_foreach=self._flow_contains_foreach,
              )
              self.metaflow_run_id = AIRFLOW_MACROS.RUN_ID
              self.metaflow_task_id = AIRFLOW_MACROS.create_task_id(
                  self._flow_contains_foreach
              )
              self.metaflow_attempt = AIRFLOW_MACROS.ATTEMPT
              self.metaflow_step_name = AIRFLOW_MACROS.STEPNAME
              self.metaflow_flow_name = self._flow_name
  
          def execute(self, context):
              result = super().execute(context)
              if result is None:
                  return
              ti = context["ti"]
              if TASK_ID_XCOM_KEY in result:
                  ti.xcom_push(
                      key=TASK_ID_XCOM_KEY,
                      value=result[TASK_ID_XCOM_KEY],
                  )
              if FOREACH_CARDINALITY_XCOM_KEY in result:
                  return list(
                      range(result[FOREACH_CARDINALITY_XCOM_KEY])
                  )
  
      return MetaflowKubernetesOperator
  
imports: import hashlib
  import json
  import sys
  import platform
  from collections import defaultdict
  from datetime import datetime, timedelta
label_desc: Use image node_id 15 for calling a global function with example usage: get_metaflow_kubernetes_operator() and returns: MetaflowKubernetesOperator, list