code: def __init__(
      self,
      max_depth=None,
      max_leaves=None,
      max_bin=None,
      grow_policy=None,
      learning_rate=None,
      n_estimators=None,
      verbosity=None,
      objective=None,
      booster=None,
      tree_method=None,
      n_jobs=None,
      gamma=None,
      min_child_weight=None,
      max_delta_step=None,
      subsample=None,
      sampling_method=None,
      colsample_bytree=None,
      colsample_bylevel=None,
      colsample_bynode=None,
      reg_alpha=None,
      reg_lambda=None,
      scale_pos_weight=None,
      base_score=None,
      random_state=None,
      missing=None,
      num_parallel_tree=None,
      monotone_constraints=None,
      interaction_constraints=None,
      importance_type=None,
      device=None,
      validate_parameters=None,
      enable_categorical=False,
      feature_types=None,
      max_cat_to_onehot=None,
      max_cat_threshold=None,
      multi_strategy=None,
      eval_metric=None,
      early_stopping_rounds=None,
      callbacks=None,
      **kwargs,
  ):
      self.n_estimators = n_estimators
      self.objective = objective
  
      self.max_depth = max_depth
      self.max_leaves = max_leaves
      self.max_bin = max_bin
      self.grow_policy = grow_policy
      self.learning_rate = learning_rate
      self.verbosity = verbosity
      self.booster = booster
      self.tree_method = tree_method
      self.gamma = gamma
      self.min_child_weight = min_child_weight
      self.max_delta_step = max_delta_step
      self.subsample = subsample
      self.sampling_method = sampling_method
      self.colsample_bytree = colsample_bytree
      self.colsample_bylevel = colsample_bylevel
      self.colsample_bynode = colsample_bynode
      self.reg_alpha = reg_alpha
      self.reg_lambda = reg_lambda
      self.scale_pos_weight = scale_pos_weight
      self.base_score = base_score
      self.missing = missing
      self.num_parallel_tree = num_parallel_tree
      self.random_state = random_state
      self.n_jobs = n_jobs
      self.monotone_constraints = monotone_constraints
      self.interaction_constraints = interaction_constraints
      self.importance_type = importance_type
      self.device = device
      self.validate_parameters = validate_parameters
      self.enable_categorical = enable_categorical
      self.feature_types = feature_types
      self.max_cat_to_onehot = max_cat_to_onehot
      self.max_cat_threshold = max_cat_threshold
      self.multi_strategy = multi_strategy
      self.eval_metric = eval_metric
      self.early_stopping_rounds = early_stopping_rounds
      self.callbacks = callbacks
      self.compiled = False
  
      if kwargs:
          self.kwargs = kwargs
  
file_path: ivy/ivy/functional/frontends/xgboost/sklearn.py
imports: import ivy
  from ivy.functional.frontends.sklearn.base import BaseEstimator
  from ivy.functional.frontends.sklearn.base import ClassifierMixin
  from .training import train
  from .core import Booster
label_desc: Use image node_id 1 to create a new XGBModel object from inherited base classes: XGBModelBase with example: obj = XGBModel(max_depth, max_leaves, max_bin, grow_policy, learning_rate, n_estimators, verbosity, objective, booster, tree_method, n_jobs, gamma, min_child_weight, max_delta_step, subsample, sampling_method, colsample_bytree, colsample_bylevel, colsample_bynode, reg_alpha, reg_lambda, scale_pos_weight, base_score, random_state, missing, num_parallel_tree, monotone_constraints, interaction_constraints, importance_type, device, validate_parameters, enable_categorical, feature_types, max_cat_to_onehot, max_cat_threshold, multi_strategy, eval_metric, early_stopping_rounds, callbacks)