code: def _get_relevant_columns(h2o_model):
      names = (
          h2o_model._model_json["output"]["original_names"]
          or h2o_model._model_json["output"]["names"]
      )
      response_column = h2o_model.actual_params.get("response_column")
      ignored_columns = (
          h2o_model.actual_params.get("ignored_columns") or []
      )
      irrelevant_columns = (
          ignored_columns + [response_column]
          if response_column
          else ignored_columns
      )
      relevant_columns = [
          feature
          for feature in names
          if feature not in irrelevant_columns
      ]
      return relevant_columns
  
imports: import logging
  import os
  import tempfile
  import pandas
  import subprocess
  import sys
  import yaml
  import mlflow
  from mlflow import pyfunc
  from mlflow.models import Model
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.utils import _save_example
  from mlflow.models import ModelSignature, ModelInputExample
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import write_to
  from mlflow.utils.model_utils import _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 6 for calling a global function with example usage: _get_relevant_columns(h2o_model) and returns: relevant_columns