class_docstr: Wrapper for boosting models which limits the number of estimators being used in the prediction.
code: def n_estimators(cls, model):
      model = get_model_of_pipeline(model)
      model_class = model.__class__.__name__
      n_estimator = None
      if model_class in [
          "AdaBoostClassifier",
          "GradientBoostingClassifier",
          "AdaBoostRegressor",
          "GradientBoostingRegressor",
      ]:
          n_estimator = len(model.estimators_)
      elif model_class in ["LGBMClassifier", "LGBMRegressor"]:
          n_estimator = model.n_estimators
      elif model_class in ["XGBClassifier", "XGBRegressor"]:
          n_estimator = model.n_estimators
      elif model_class in ["CatBoostClassifier", "CatBoostRegressor"]:
          n_estimator = model.tree_count_
      else:
          cls._raise_not_supported_model_error(model_class=model_class)
  
      if n_estimator is None:
          raise ModelValidationError(
              "Could not extract number of estimators from model"
          )
  
      return n_estimator
  
imports: from copy import deepcopy
  from typing import TYPE_CHECKING, Callable, Tuple, Union
  import numpy
  import plotly.graph_objects
  from sklearn.pipeline import Pipeline
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.core.errors import DeepchecksValueError, ModelValidationError
  from deepchecks.tabular import Context, TrainTestCheck
  from deepchecks.utils.docref import doclink
  from deepchecks.utils.model import get_model_of_pipeline
  from deepchecks.utils.strings import format_percent
label_desc: Use image node_id 5 for calling the PartialBoostingModel obj's underlying member method code with example usage: obj.n_estimators(cls, model) and returns: n_estimator