code: def _load_model(path):
      flavor_conf = _get_flavor_configuration(
          model_path=path, flavor_name=FLAVOR_NAME
      )
      model_type = flavor_conf["model_type"]
      model_file = flavor_conf["model_file"]
      extra_prediction_args = flavor_conf["extra_prediction_args"]
      relevant_columns = flavor_conf["relevant_columns"]
      return _H2OModelWrapper(
          model_file,
          model_type,
          path,
          extra_prediction_args,
          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 10 for calling a global function with example usage: _load_model(path) and returns: _H2OModelWrapper