class_docstr: Wrapper for boosting models which limits the number of estimators being used in the prediction.
code: def _raise_not_supported_model_error(cls, model_class):
      if model_class != "_DummyModel":
          raise ModelValidationError(
              cls._UNSUPPORTED_MODEL_ERROR.format(
                  supported_models=cls._SUPPORTED_MODELS,
                  model_type=model_class,
              )
          )
      else:
          raise ModelValidationError(cls._NO_MODEL_ERROR)
  
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 2 for calling the PartialBoostingModel obj's underlying member method code with example usage: obj._raise_not_supported_model_error(cls, model_class) without return types