code: def save_model(
      cb_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,
      **kwargs,
  ):
      """Save a CatBoost model to a path on the local file system.
  
      Args:
          cb_model: CatBoost model (an instance of `CatBoost`_, `CatBoostClassifier`_,
              `CatBoostRanker`_, or `CatBoostRegressor`_) 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.
          kwargs: kwargs to pass to `CatBoost.save_model`_ method.
  
      """
      import catboost as cb
  
      _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 = _CatboostModelWrapper(cb_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)
      cb_model.save_model(model_data_path, **kwargs)
  
      model_bin_kwargs = {_MODEL_BINARY_KEY: _MODEL_BINARY_FILE_NAME}
      pyfunc.add_to_model(
          mlflow_model,
          loader_module="mlflow.catboost",
          conda_env=_CONDA_ENV_FILE_NAME,
          python_env=_PYTHON_ENV_FILE_NAME,
          code=code_dir_subpath,
          **model_bin_kwargs,
      )
  
      flavor_conf = {
          _MODEL_TYPE_KEY: cb_model.__class__.__name__,
          _SAVE_FORMAT_KEY: kwargs.get("format", "cbm"),
          **model_bin_kwargs,
      }
      mlflow_model.add_flavor(
          FLAVOR_NAME,
          catboost_version=cb.__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:
          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 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 3 for calling a global function with example usage: save_model(cb_model, path, conda_env, code_paths, mlflow_model, signature, input_example, pip_requirements, extra_pip_requirements, metadata) without return types