code: def log_model(
      cb_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,
      **kwargs,
  ):
      """Log a CatBoost model as an MLflow artifact for the current run.
  
      Args:
          cb_model: CatBoost model (an instance of `CatBoost`_, `CatBoostClassifier`_,
              `CatBoostRanker`_, or `CatBoostRegressor`_) 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: {{ signature }}
          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.
          kwargs: kwargs to pass to `CatBoost.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.catboost,
          registered_model_name=registered_model_name,
          cb_model=cb_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,
          **kwargs,
      )
  
imports: import contextlib
  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(cb_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