code: def _load_model(path, init=False):
      import h2o
  
      path = os.path.abspath(path)
      with open(os.path.join(path, "h2o.yaml")) as f:
          params = yaml.safe_load(f.read())
      if init:
          h2o.init(**(params["init"] if "init" in params else {}))
          h2o.no_progress()
  
      model_path = os.path.join(path, params["model_file"])
      if hasattr(h2o, "upload_model"):
          model = h2o.upload_model(model_path)
      else:
          warnings.warn(
              "If your cluster is remote, H2O may not load the model correctly. "
              "Please upgrade H2O version to a newer version"
          )
          model = h2o.load_model(model_path)
  
      return model
  
imports: import logging
  import os
  import warnings
  from typing import Any, Dict, Optional
  import yaml
  import mlflow
  from mlflow import pyfunc
  from mlflow.models import Model, ModelInputExample, ModelSignature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.signature import _infer_signature_from_input_example
  from mlflow.models.utils import _save_example
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
  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 get_total_file_size, write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 5 for calling a global function with example usage: _load_model(path, init) and returns: model