code: def train(
      params: Dict[str, Any],
      train_set: Dataset,
      num_boost_round: int = 100,
      valid_sets: Optional[List[Dataset]] = None,
      valid_names: Optional[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",
      keep_training_booster: bool = False,
      callbacks: Optional[List[Callable]] = None,
  ) -> Booster:
      """Perform the training 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.
      valid_sets : list of Dataset, or None, optional (default=None)
          List of data to be evaluated on during training.
      valid_names : list of str, or None, optional (default=None)
          Names of ``valid_sets``.
      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 whitespaces).
              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 the ``metric`` parameter to the string ``"None"`` in ``params``.
      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.
      keep_training_booster : bool, optional (default=False)
          Whether the returned Booster will be used to keep training.
          If False, the returned value will be converted into _InnerPredictor before returning.
          This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster.
          When your model is very large and cause the memory error,
          you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``.
          You can still use _InnerPredictor as ``init_model`` for future continue training.
      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.
  
      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
      -------
      booster : Booster
          The trained Booster model.
      """
      if not isinstance(train_set, Dataset):
          raise TypeError(
              f"train() 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}."
          )
  
      if isinstance(valid_sets, list):
          for i, valid_item in enumerate(valid_sets):
              if not isinstance(valid_item, Dataset):
                  raise TypeError(
                      "Every item in valid_sets must be a Dataset object. "
                      f"Item {i} has type '{type(valid_item).__name__}'."
                  )
  
      # create predictor first
      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:
              num_boost_round = params.pop(alias)
              _log_warning(
                  f"Found `{alias}` in params. Will use it instead of argument"
              )
      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)
  
      predictor: Optional[_InnerPredictor] = None
      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),
          )
  
      if predictor is not None:
          init_iteration = predictor.current_iteration()
      else:
          init_iteration = 0
  
      train_set._update_params(params)._set_predictor(
          predictor
      ).set_feature_name(feature_name).set_categorical_feature(
          categorical_feature
      )
  
      is_valid_contain_train = False
      train_data_name = "training"
      reduced_valid_sets = []
      name_valid_sets = []
      if valid_sets is not None:
          if isinstance(valid_sets, Dataset):
              valid_sets = [valid_sets]
          if isinstance(valid_names, str):
              valid_names = [valid_names]
          for i, valid_data in enumerate(valid_sets):
              # reduce cost for prediction training data
              if valid_data is train_set:
                  is_valid_contain_train = True
                  if valid_names is not None:
                      train_data_name = valid_names[i]
                  continue
              reduced_valid_sets.append(
                  valid_data._update_params(params).set_reference(
                      train_set
                  )
              )
              if valid_names is not None and len(valid_names) > i:
                  name_valid_sets.append(valid_names[i])
              else:
                  name_valid_sets.append(f"valid_{i}")
      # process 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")
      )
  
      # construct booster
      try:
          booster = Booster(params=params, train_set=train_set)
          if is_valid_contain_train:
              booster.set_train_data_name(train_data_name)
          for valid_set, name_valid_set in zip(
              reduced_valid_sets, name_valid_sets
          ):
              booster.add_valid(valid_set, name_valid_set)
      finally:
          train_set._reverse_update_params()
          for valid_set in reduced_valid_sets:
              valid_set._reverse_update_params()
      booster.best_iteration = 0
  
      # start training
      for i in range(init_iteration, init_iteration + num_boost_round):
          for cb in callbacks_before_iter:
              cb(
                  callback.CallbackEnv(
                      model=booster,
                      params=params,
                      iteration=i,
                      begin_iteration=init_iteration,
                      end_iteration=init_iteration + num_boost_round,
                      evaluation_result_list=None,
                  )
              )
  
          booster.update(fobj=fobj)
  
          evaluation_result_list: List[
              _LGBM_BoosterEvalMethodResultType
          ] = []
          # check evaluation result.
          if valid_sets is not None:
              if is_valid_contain_train:
                  evaluation_result_list.extend(
                      booster.eval_train(feval)
                  )
              evaluation_result_list.extend(booster.eval_valid(feval))
          try:
              for cb in callbacks_after_iter:
                  cb(
                      callback.CallbackEnv(
                          model=booster,
                          params=params,
                          iteration=i,
                          begin_iteration=init_iteration,
                          end_iteration=init_iteration
                          + num_boost_round,
                          evaluation_result_list=evaluation_result_list,
                      )
                  )
          except callback.EarlyStopException as earlyStopException:
              booster.best_iteration = (
                  earlyStopException.best_iteration + 1
              )
              evaluation_result_list = earlyStopException.best_score
              break
      booster.best_score = defaultdict(OrderedDict)
      for dataset_name, eval_name, score, _ in evaluation_result_list:
          booster.best_score[dataset_name][eval_name] = score
      if not keep_training_booster:
          booster.model_from_string(
              booster.model_to_string()
          ).free_dataset()
      return booster
  
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 1 for calling a global function with example usage: train(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, feature_name, categorical_feature, keep_training_booster, callbacks) and returns: booster