code: def cv(
      params: Dict[str, Any],
      train_set: Dataset,
      num_boost_round: int = 100,
      folds: Optional[
          Union[
              Iterable[Tuple[np.ndarray, np.ndarray]],
              _LGBMBaseCrossValidator,
          ]
      ] = None,
      nfold: int = 5,
      stratified: bool = True,
      shuffle: bool = True,
      metrics: Optional[Union[str, List[str]]] = None,
      feval: Optional[
          Union[
              _LGBM_CustomMetricFunction,
              List[_LGBM_CustomMetricFunction],
          ]
      ] = None,
      init_model: Optional[Union[str, Path, Booster]] = None,
      feature_name: _LGBM_FeatureNameConfiguration = "auto",
      categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto",
      fpreproc: Optional[_LGBM_PreprocFunction] = None,
      seed: int = 0,
      callbacks: Optional[List[Callable]] = None,
      eval_train_metric: bool = False,
      return_cvbooster: bool = False,
  ) -> Dict[str, Union[List[float], CVBooster]]:
      """Perform the cross-validation with given parameters.
  
      Parameters
      ----------
      params : dict
          Parameters for training. Values passed through ``params`` take precedence over those
          supplied via arguments.
      train_set : Dataset
          Data to be trained on.
      num_boost_round : int, optional (default=100)
          Number of boosting iterations.
      folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None)
          If generator or iterator, it should yield the train and test indices for each fold.
          If object, it should be one of the scikit-learn splitter classes
          (https://scikit-learn.org/stable/modules/classes.html#splitter-classes)
          and have ``split`` method.
          This argument has highest priority over other data split arguments.
      nfold : int, optional (default=5)
          Number of folds in CV.
      stratified : bool, optional (default=True)
          Whether to perform stratified sampling.
      shuffle : bool, optional (default=True)
          Whether to shuffle before splitting data.
      metrics : str, list of str, or None, optional (default=None)
          Evaluation metrics to be monitored while CV.
          If not None, the metric in ``params`` will be overridden.
      feval : callable, list of callable, or None, optional (default=None)
          Customized evaluation function.
          Each evaluation function should accept two parameters: preds, eval_data,
          and return (eval_name, eval_result, is_higher_better) or list of such tuples.
  
              preds : numpy 1-D array or numpy 2-D array (for multi-class task)
                  The predicted values.
                  For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes].
                  If custom objective function is used, predicted values are returned before any transformation,
                  e.g. they are raw margin instead of probability of positive class for binary task in this case.
              eval_data : Dataset
                  A ``Dataset`` to evaluate.
              eval_name : str
                  The name of evaluation function (without whitespace).
              eval_result : float
                  The eval result.
              is_higher_better : bool
                  Is eval result higher better, e.g. AUC is ``is_higher_better``.
  
          To ignore the default metric corresponding to the used objective,
          set ``metrics`` to the string ``"None"``.
      init_model : str, pathlib.Path, Booster or None, optional (default=None)
          Filename of LightGBM model or Booster instance used for continue training.
      feature_name : list of str, or 'auto', optional (default="auto")
          Feature names.
          If 'auto' and data is pandas DataFrame, data columns names are used.
      categorical_feature : list of str or int, or 'auto', optional (default="auto")
          Categorical features.
          If list of int, interpreted as indices.
          If list of str, interpreted as feature names (need to specify ``feature_name`` as well).
          If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
          All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).
          Large values could be memory consuming. Consider using consecutive integers starting from zero.
          All negative values in categorical features will be treated as missing values.
          The output cannot be monotonically constrained with respect to a categorical feature.
          Floating point numbers in categorical features will be rounded towards 0.
      fpreproc : callable or None, optional (default=None)
          Preprocessing function that takes (dtrain, dtest, params)
          and returns transformed versions of those.
      seed : int, optional (default=0)
          Seed used to generate the folds (passed to numpy.random.seed).
      callbacks : list of callable, or None, optional (default=None)
          List of callback functions that are applied at each iteration.
          See Callbacks in Python API for more information.
      eval_train_metric : bool, optional (default=False)
          Whether to display the train metric in progress.
          The score of the metric is calculated again after each training step, so there is some impact on performance.
      return_cvbooster : bool, optional (default=False)
          Whether to return Booster models trained on each fold through ``CVBooster``.
  
      Note
      ----
      A custom objective function can be provided for the ``objective`` parameter.
      It should accept two parameters: preds, train_data and return (grad, hess).
  
          preds : numpy 1-D array or numpy 2-D array (for multi-class task)
              The predicted values.
              Predicted values are returned before any transformation,
              e.g. they are raw margin instead of probability of positive class for binary task.
          train_data : Dataset
              The training dataset.
          grad : numpy 1-D array or numpy 2-D array (for multi-class task)
              The value of the first order derivative (gradient) of the loss
              with respect to the elements of preds for each sample point.
          hess : numpy 1-D array or numpy 2-D array (for multi-class task)
              The value of the second order derivative (Hessian) of the loss
              with respect to the elements of preds for each sample point.
  
      For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes],
      and grad and hess should be returned in the same format.
  
      Returns
      -------
      eval_results : dict
          History of evaluation results of each metric.
          The dictionary has the following format:
          {'valid metric1-mean': [values], 'valid metric1-stdv': [values],
          'valid metric2-mean': [values], 'valid metric2-stdv': [values],
          ...}.
          If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key.
          If ``eval_train_metric=True``, also returns the train metric history.
          In this case, the dictionary has the following format:
          {'train metric1-mean': [values], 'valid metric1-mean': [values],
          'train metric2-mean': [values], 'valid metric2-mean': [values],
          ...}.
      """
      if not isinstance(train_set, Dataset):
          raise TypeError(
              f"cv() only accepts Dataset object, train_set has type '{type(train_set).__name__}'."
          )
  
      if num_boost_round <= 0:
          raise ValueError(
              f"num_boost_round must be greater than 0. Got {num_boost_round}."
          )
  
      params = copy.deepcopy(params)
      params = _choose_param_value(
          main_param_name="objective", params=params, default_value=None
      )
      fobj: Optional[_LGBM_CustomObjectiveFunction] = None
      if callable(params["objective"]):
          fobj = params["objective"]
          params["objective"] = "none"
      for alias in _ConfigAliases.get("num_iterations"):
          if alias in params:
              _log_warning(
                  f"Found '{alias}' in params. Will use it instead of 'num_boost_round' argument"
              )
              num_boost_round = params.pop(alias)
      params["num_iterations"] = num_boost_round
      # setting early stopping via global params should be possible
      params = _choose_param_value(
          main_param_name="early_stopping_round",
          params=params,
          default_value=None,
      )
      if params["early_stopping_round"] is None:
          params.pop("early_stopping_round")
      first_metric_only = params.get("first_metric_only", False)
  
      if isinstance(init_model, (str, Path)):
          predictor = _InnerPredictor.from_model_file(
              model_file=init_model, pred_parameter=params
          )
      elif isinstance(init_model, Booster):
          predictor = _InnerPredictor.from_booster(
              booster=init_model,
              pred_parameter=dict(init_model.params, **params),
          )
      else:
          predictor = None
  
      if metrics is not None:
          for metric_alias in _ConfigAliases.get("metric"):
              params.pop(metric_alias, None)
          params["metric"] = metrics
  
      train_set._update_params(params)._set_predictor(
          predictor
      ).set_feature_name(feature_name).set_categorical_feature(
          categorical_feature
      )
  
      results = defaultdict(list)
      cvfolds = _make_n_folds(
          full_data=train_set,
          folds=folds,
          nfold=nfold,
          params=params,
          seed=seed,
          fpreproc=fpreproc,
          stratified=stratified,
          shuffle=shuffle,
          eval_train_metric=eval_train_metric,
      )
  
      # setup callbacks
      if callbacks is None:
          callbacks_set = set()
      else:
          for i, cb in enumerate(callbacks):
              cb.__dict__.setdefault("order", i - len(callbacks))
          callbacks_set = set(callbacks)
  
      if "early_stopping_round" in params:
          callbacks_set.add(
              callback.early_stopping(
                  stopping_rounds=params["early_stopping_round"],  # type: ignore[arg-type]
                  first_metric_only=first_metric_only,
                  verbose=_choose_param_value(
                      main_param_name="verbosity",
                      params=params,
                      default_value=1,
                  ).pop("verbosity")
                  > 0,
              )
          )
  
      callbacks_before_iter_set = {
          cb
          for cb in callbacks_set
          if getattr(cb, "before_iteration", False)
      }
      callbacks_after_iter_set = (
          callbacks_set - callbacks_before_iter_set
      )
      callbacks_before_iter = sorted(
          callbacks_before_iter_set, key=attrgetter("order")
      )
      callbacks_after_iter = sorted(
          callbacks_after_iter_set, key=attrgetter("order")
      )
  
      for i in range(num_boost_round):
          for cb in callbacks_before_iter:
              cb(
                  callback.CallbackEnv(
                      model=cvfolds,
                      params=params,
                      iteration=i,
                      begin_iteration=0,
                      end_iteration=num_boost_round,
                      evaluation_result_list=None,
                  )
              )
          cvfolds.update(fobj=fobj)  # type: ignore[call-arg]
          res = _agg_cv_result(cvfolds.eval_valid(feval))  # type: ignore[call-arg]
          for _, key, mean, _, std in res:
              results[f"{key}-mean"].append(mean)
              results[f"{key}-stdv"].append(std)
          try:
              for cb in callbacks_after_iter:
                  cb(
                      callback.CallbackEnv(
                          model=cvfolds,
                          params=params,
                          iteration=i,
                          begin_iteration=0,
                          end_iteration=num_boost_round,
                          evaluation_result_list=res,
                      )
                  )
          except callback.EarlyStopException as earlyStopException:
              cvfolds.best_iteration = (
                  earlyStopException.best_iteration + 1
              )
              for bst in cvfolds.boosters:
                  bst.best_iteration = cvfolds.best_iteration
              for k in results:
                  results[k] = results[k][: cvfolds.best_iteration]
              break
  
      if return_cvbooster:
          results["cvbooster"] = cvfolds  # type: ignore[assignment]
  
      return dict(results)
  
imports: import copy
  import json
  from collections import OrderedDict, defaultdict
  from operator import attrgetter
  from pathlib import Path
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
  import numpy
  from .None import callback
  from .basic import Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _InnerPredictor, _LGBM_BoosterEvalMethodResultType, _LGBM_BoosterEvalMethodResultWithStandardDeviationType, _LGBM_CategoricalFeatureConfiguration, _LGBM_CustomObjectiveFunction, _LGBM_EvalFunctionResultType, _LGBM_FeatureNameConfiguration, _log_warning
  from .compat import SKLEARN_INSTALLED, _LGBMBaseCrossValidator, _LGBMGroupKFold, _LGBMStratifiedKFold
label_desc: Use image node_id 4 for calling a global function with example usage: cv(params, train_set, num_boost_round, folds, nfold, stratified, shuffle, metrics, feval, init_model, feature_name, categorical_feature, fpreproc, seed, callbacks, eval_train_metric, return_cvbooster) and returns: dict