code: def _step_cli(self, node, paths, code_package_url, user_code_retries):
      cmds = []
  
      script_name = os.path.basename(sys.argv[0])
      executable = self.environment.executable(node.name)
  
      entrypoint = [executable, script_name]
  
      top_opts_dict = {
          "with": [
              decorator.make_decorator_spec()
              for decorator in node.decorators
              if not decorator.statically_defined
          ]
      }
      # FlowDecorators can define their own top-level options. They are
      # responsible for adding their own top-level options and values through
      # the get_top_level_options() hook. See similar logic in runtime.py.
      for deco in flow_decorators():
          top_opts_dict.update(deco.get_top_level_options())
  
      top_opts = list(dict_to_cli_options(top_opts_dict))
  
      top_level = top_opts + [
          "--quiet",
          "--metadata=%s" % self.metadata.TYPE,
          "--environment=%s" % self.environment.TYPE,
          "--datastore=%s" % self.flow_datastore.TYPE,
          "--datastore-root=%s" % self.flow_datastore.datastore_root,
          "--event-logger=%s" % self.event_logger.TYPE,
          "--monitor=%s" % self.monitor.TYPE,
          "--no-pylint",
          "--with=airflow_internal",
      ]
  
      if node.name == "start":
          # We need a separate unique ID for the special _parameters task
          task_id_params = "%s-params" % AIRFLOW_MACROS.create_task_id(
              self.contains_foreach
          )
          # Export user-defined parameters into runtime environment
          param_file = "".join(
              random.choice(string.ascii_lowercase) for _ in range(10)
          )
          # Setup Parameters as environment variables which are stored in a dictionary.
          export_params = (
              "python -m "
              "metaflow.plugins.airflow.plumbing.set_parameters %s "
              "&& . `pwd`/%s" % (param_file, param_file)
          )
          # Setting parameters over here.
          params = (
              entrypoint
              + top_level
              + [
                  "init",
                  "--run-id %s" % AIRFLOW_MACROS.RUN_ID,
                  "--task-id %s" % task_id_params,
              ]
          )
  
          # Assign tags to run objects.
          if self.tags:
              params.extend("--tag %s" % tag for tag in self.tags)
  
          # If the start step gets retried, we must be careful not to
          # regenerate multiple parameters tasks. Hence, we check first if
          # _parameters exists already.
          exists = entrypoint + [
              # Dump the parameters task
              "dump",
              "--max-value-size=0",
              "%s/_parameters/%s"
              % (AIRFLOW_MACROS.RUN_ID, task_id_params),
          ]
          cmd = "if ! %s >/dev/null 2>/dev/null; then %s && %s; fi" % (
              " ".join(exists),
              export_params,
              " ".join(params),
          )
          cmds.append(cmd)
          # set input paths for parameters
          paths = "%s/_parameters/%s" % (
              AIRFLOW_MACROS.RUN_ID,
              task_id_params,
          )
  
      step = [
          "step",
          node.name,
          "--run-id %s" % AIRFLOW_MACROS.RUN_ID,
          "--task-id %s"
          % AIRFLOW_MACROS.create_task_id(self.contains_foreach),
          "--retry-count %s" % AIRFLOW_MACROS.ATTEMPT,
          "--max-user-code-retries %d" % user_code_retries,
          "--input-paths %s" % paths,
      ]
      if self.tags:
          step.extend("--tag %s" % tag for tag in self.tags)
      if self.namespace is not None:
          step.append("--namespace=%s" % self.namespace)
  
      parent_is_foreach = (
          any(  # The immediate parent is a foreach node.
              self.graph[n].type == "foreach" for n in node.in_funcs
          )
      )
      if parent_is_foreach:
          step.append(
              "--split-index %s" % AIRFLOW_MACROS.FOREACH_SPLIT_INDEX
          )
  
      cmds.append(" ".join(entrypoint + top_level + step))
      return cmds
  
imports: import json
  import os
  import random
  import string
  import sys
  from datetime import datetime, timedelta
  from io import BytesIO
  import metaflow.util
  from metaflow import current
  from metaflow.decorators import flow_decorators
  from metaflow.exception import MetaflowException
  from metaflow.includefile import FilePathClass
  from metaflow.metaflow_config import AIRFLOW_KUBERNETES_CONN_ID, AIRFLOW_KUBERNETES_KUBECONFIG_CONTEXT, AIRFLOW_KUBERNETES_KUBECONFIG_FILE, AIRFLOW_KUBERNETES_STARTUP_TIMEOUT_SECONDS, AWS_SECRETS_MANAGER_DEFAULT_REGION, AZURE_STORAGE_BLOB_SERVICE_ENDPOINT, CARD_AZUREROOT, CARD_GSROOT, CARD_S3ROOT, DATASTORE_SYSROOT_AZURE, DATASTORE_SYSROOT_GS, DATASTORE_SYSROOT_S3, DATATOOLS_S3ROOT, DEFAULT_SECRETS_BACKEND_TYPE, KUBERNETES_SECRETS, KUBERNETES_SERVICE_ACCOUNT, S3_ENDPOINT_URL, SERVICE_HEADERS, SERVICE_INTERNAL_URL
  from metaflow.metaflow_config_funcs import config_values
  from metaflow.parameters import DelayedEvaluationParameter, JSONTypeClass, deploy_time_eval
  from metaflow.plugins.cards.card_modules import chevron
  from metaflow.plugins.kubernetes.kubernetes import Kubernetes
  from metaflow.plugins.timeout_decorator import get_run_time_limit_for_task
  from metaflow.util import compress_list, dict_to_cli_options, get_username
  from .None import airflow_utils
  from .airflow_utils import AIRFLOW_MACROS, TASK_ID_XCOM_KEY, AirflowTask, Workflow
  from .exception import AirflowException
  from .sensors import SUPPORTED_SENSORS
label_desc: Use image node_id 13 for calling the Airflow obj's underlying member method code with example usage: obj._step_cli(node, paths, code_package_url, user_code_retries) and returns: cmds