code: def save_model(
      h2o_model,
      path,
      conda_env=None,
      code_paths=None,
      mlflow_model=None,
      settings=None,
      signature: ModelSignature = None,
      input_example: ModelInputExample = None,
      pip_requirements=None,
      extra_pip_requirements=None,
      metadata=None,
  ):
      """Save an H2O model to a path on the local file system.
  
      Args:
          h2o_model: H2O model to be saved.
          path: Local path where the model is to be saved.
          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.
          mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
          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.
      """
      import h2o
  
      _validate_env_arguments(
          conda_env, pip_requirements, extra_pip_requirements
      )
  
      path = os.path.abspath(path)
      _validate_and_prepare_target_save_path(path)
      model_data_subpath = "model.h2o"
      model_data_path = os.path.join(path, model_data_subpath)
      os.makedirs(model_data_path)
      code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
  
      if signature is None and input_example is not None:
          wrapped_model = _H2OModelWrapper(h2o_model)
          signature = _infer_signature_from_input_example(
              input_example, wrapped_model
          )
      elif signature is False:
          signature = None
  
      if mlflow_model is None:
          mlflow_model = Model()
      if signature is not None:
          mlflow_model.signature = signature
      if input_example is not None:
          _save_example(mlflow_model, input_example, path)
      if metadata is not None:
          mlflow_model.metadata = metadata
  
      # Save h2o-model
      if hasattr(h2o, "download_model"):
          h2o_save_location = h2o.download_model(
              model=h2o_model, path=model_data_path
          )
      else:
          warnings.warn(
              "If your cluster is remote, H2O may not store the model correctly. "
              "Please upgrade H2O version to a newer version"
          )
          h2o_save_location = h2o.save_model(
              model=h2o_model, path=model_data_path, force=True
          )
      model_file = os.path.basename(h2o_save_location)
  
      # Save h2o-settings
      if settings is None:
          settings = {}
      settings["full_file"] = h2o_save_location
      settings["model_file"] = model_file
      settings["model_dir"] = model_data_path
      with open(
          os.path.join(model_data_path, "h2o.yaml"), "w"
      ) as settings_file:
          yaml.safe_dump(settings, stream=settings_file)
  
      pyfunc.add_to_model(
          mlflow_model,
          loader_module="mlflow.h2o",
          data=model_data_subpath,
          conda_env=_CONDA_ENV_FILE_NAME,
          python_env=_PYTHON_ENV_FILE_NAME,
          code=code_dir_subpath,
      )
      mlflow_model.add_flavor(
          FLAVOR_NAME,
          h2o_version=h2o.__version__,
          data=model_data_subpath,
          code=code_dir_subpath,
      )
      if size := get_total_file_size(path):
          mlflow_model.model_size_bytes = size
      mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
  
      if conda_env is None:
          if pip_requirements is None:
              default_reqs = get_default_pip_requirements()
              # To ensure `_load_pyfunc` can successfully load the model during the dependency
              # inference, `mlflow_model.save` must be called beforehand to save an MLmodel file.
              inferred_reqs = mlflow.models.infer_pip_requirements(
                  path,
                  FLAVOR_NAME,
                  fallback=default_reqs,
              )
              default_reqs = sorted(
                  set(inferred_reqs).union(default_reqs)
              )
          else:
              default_reqs = None
          (
              conda_env,
              pip_requirements,
              pip_constraints,
          ) = _process_pip_requirements(
              default_reqs,
              pip_requirements,
              extra_pip_requirements,
          )
      else:
          (
              conda_env,
              pip_requirements,
              pip_constraints,
          ) = _process_conda_env(conda_env)
  
      with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
          yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
  
      # Save `constraints.txt` if necessary
      if pip_constraints:
          write_to(
              os.path.join(path, _CONSTRAINTS_FILE_NAME),
              "\n".join(pip_constraints),
          )
  
      # Save `requirements.txt`
      write_to(
          os.path.join(path, _REQUIREMENTS_FILE_NAME),
          "\n".join(pip_requirements),
      )
  
      _PythonEnv.current().to_yaml(
          os.path.join(path, _PYTHON_ENV_FILE_NAME)
      )
  
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 3 for calling a global function with example usage: save_model(h2o_model, path, conda_env, code_paths, mlflow_model, settings, signature, input_example, pip_requirements, extra_pip_requirements, metadata) without return types