class_docstr: Wrapper for boosting models which limits the number of estimators being used in the prediction.
code: def predict(self, x):
      if self.model_class in [
          "AdaBoostClassifier",
          "GradientBoostingClassifier",
          "AdaBoostRegressor",
          "GradientBoostingRegressor",
      ]:
          return self.model.predict(x)
      elif self.model_class in ["LGBMClassifier", "LGBMRegressor"]:
          return self.model.predict(x, num_iteration=self.step)
      elif self.model_class in ["XGBClassifier", "XGBRegressor"]:
          return self.model.predict(x, iteration_range=(0, self.step))
      elif self.model_class in [
          "CatBoostClassifier",
          "CatBoostRegressor",
      ]:
          return self.model.predict(x, ntree_end=self.step)
      else:
          self._raise_not_supported_model_error(self.model_class)
  
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 4 for calling the PartialBoostingModel obj's underlying member method code with example usage: obj.predict(x) and returns: self, self, self, self