code: def log_model(
      pr_model,
      artifact_path,
      conda_env=None,
      code_paths=None,
      registered_model_name=None,
      signature: ModelSignature = None,
      input_example: ModelInputExample = None,
      await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
      pip_requirements=None,
      extra_pip_requirements=None,
      metadata=None,
  ):
      """
      Logs a Prophet model as an MLflow artifact for the current run.
  
      Args:
          pr_model: Prophet 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: This argument may change or be removed in a
              future release without warning. 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: 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 }}
          await_registration_for: Number of seconds to wait for the model version
              to finish being created and is in ``READY`` status.
              By default, the function waits for five minutes.
              Specify 0 or None to skip waiting.
          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.
  
      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.prophet,
          registered_model_name=registered_model_name,
          pr_model=pr_model,
          conda_env=conda_env,
          code_paths=code_paths,
          signature=signature,
          input_example=input_example,
          await_registration_for=await_registration_for,
          pip_requirements=pip_requirements,
          extra_pip_requirements=extra_pip_requirements,
          metadata=metadata,
      )
  
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 4 for calling a global function with example usage: log_model(pr_model, artifact_path, conda_env, code_paths, registered_model_name, signature, input_example, await_registration_for, pip_requirements, extra_pip_requirements, metadata) and returns: Model