code: def log_model(
      h2o_model,
      artifact_path,
      conda_env=None,
      code_paths=None,
      registered_model_name=None,
      signature: ModelSignature = None,
      input_example: ModelInputExample = None,
      pip_requirements=None,
      extra_pip_requirements=None,
      metadata=None,
      **kwargs,
  ):
      """Log an H2O model as an MLflow artifact for the current run.
  
      Args:
          h2o_model: H2O model to be saved.
          artifact_path: Run-relative artifact path.
          conda_env: {{ conda_env }}
          code_paths: A list of local filesystem paths to Python file dependencies (or directories
              containing file dependencies). These files are *prepended* to the system
              path when the model is loaded.
          registered_model_name: If given, create a model version under
              ``registered_model_name``, also creating a registered model if one
              with the given name does not exist.
          signature: {{ signature }}
          input_example: {{ input_example }}
          pip_requirements: {{ pip_requirements }}
          extra_pip_requirements: {{ extra_pip_requirements }}
          metadata: Custom metadata dictionary passed to the model and stored in the MLmodel file.
  
              .. Note:: Experimental: This parameter may change or be removed in a future
                  release without warning.
          kwargs: kwargs to pass to ``h2o.save_model`` method.
  
      Returns:
          A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
          metadata of the logged model.
  
      """
      return Model.log(
          artifact_path=artifact_path,
          flavor=mlflow.h2o,
          registered_model_name=registered_model_name,
          h2o_model=h2o_model,
          conda_env=conda_env,
          code_paths=code_paths,
          signature=signature,
          input_example=input_example,
          pip_requirements=pip_requirements,
          extra_pip_requirements=extra_pip_requirements,
          metadata=metadata,
          **kwargs,
      )
  
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 4 for calling a global function with example usage: log_model(h2o_model, artifact_path, conda_env, code_paths, registered_model_name, signature, input_example, pip_requirements, extra_pip_requirements, metadata) and returns: Model