code: def save_model(
      pr_model,
      path,
      conda_env=None,
      code_paths=None,
      mlflow_model=None,
      signature: ModelSignature = None,
      input_example: ModelInputExample = None,
      pip_requirements=None,
      extra_pip_requirements=None,
      metadata=None,
  ):
      """
      Save a Prophet model to a path on the local file system.
  
      Args:
          pr_model: Prophet model (an instance of Prophet() forecaster that has been fit
              on a temporal series.
          path: Local path where the serialized model (as JSON) 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: an instance of the :py:class:`ModelSignature <mlflow.models.ModelSignature>`
              class that describes the model's inputs and outputs. If not specified but an
              ``input_example`` is supplied, a signature will be automatically inferred
              based on the supplied input example and model. To disable automatic signature
              inference when providing an input example, set ``signature`` to ``False``.
              To manually infer a model signature, call
              :py:func:`infer_signature() <mlflow.models.infer_signature>` on datasets
              with valid model inputs, such as a training dataset with the target column
              omitted, and valid model outputs, like model predictions made on the training
              dataset, for example:
  
              .. code-block:: python
  
                  from mlflow.models import infer_signature
  
                  model = Prophet().fit(df)
                  train = model.history
                  predictions = model.predict(model.make_future_dataframe(30))
                  signature = infer_signature(train, predictions)
          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 prophet
  
      _validate_env_arguments(
          conda_env, pip_requirements, extra_pip_requirements
      )
  
      path = os.path.abspath(path)
      _validate_and_prepare_target_save_path(path)
      code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)
  
      if signature is None and input_example is not None:
          wrapped_model = _ProphetModelWrapper(pr_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
  
      model_data_path = os.path.join(path, _MODEL_BINARY_FILE_NAME)
      _save_model(pr_model, model_data_path)
  
      model_bin_kwargs = {_MODEL_BINARY_KEY: _MODEL_BINARY_FILE_NAME}
      pyfunc.add_to_model(
          mlflow_model,
          loader_module="mlflow.prophet",
          conda_env=_CONDA_ENV_FILE_NAME,
          python_env=_PYTHON_ENV_FILE_NAME,
          code=code_dir_subpath,
          **model_bin_kwargs,
      )
      flavor_conf = {
          _MODEL_TYPE_KEY: pr_model.__class__.__name__,
          **model_bin_kwargs,
      }
      mlflow_model.add_flavor(
          FLAVOR_NAME,
          prophet_version=prophet.__version__,
          code=code_dir_subpath,
          **flavor_conf,
      )
      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:
          default_reqs = None
          if pip_requirements is None:
              # cannot use inferred requirements due to prophet's build process
              # as the package installation of pystan requires Cython to be present
              # in the path. Prophet's installation itself requires imports of
              # existing libraries, preventing the execution of a batched pip install
              # and instead using a a strictly defined list of dependencies.
              # NOTE: if Prophet .whl build architecture is changed, this should be
              # modified to a standard inferred approach.
              default_reqs = get_default_pip_requirements()
          (
              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)
  
      if pip_constraints:
          write_to(
              os.path.join(path, _CONSTRAINTS_FILE_NAME),
              "\n".join(pip_constraints),
          )
  
      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 json
  import logging
  import os
  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._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  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(pr_model, path, conda_env, code_paths, mlflow_model, signature, input_example, pip_requirements, extra_pip_requirements, metadata) without return types