code: def is_classifier(self) -> bool:
      objective = self.booster.dump_model(num_iteration=1)["objective"]
      if "binary" in objective or "multiclass" in objective:
          return True
      elif objective in [
          "regression",
          "regression_l1",
          "huber",
          "fair",
          "poisson",
          "quantile",
          "mape",
          "gamma",
          "tweedie",
      ]:
          return False
      raise Exception(
          f"objective {objective} is not yet supported by dtreeviz's lightgbm implementation"
      )
  
imports: from collections import defaultdict
  from typing import List, Mapping
  import numpy
  import pandas
  from lightgbm.basic import Booster
  from dtreeviz.models.shadow_decision_tree import ShadowDecTree, VisualisationNotYetSupportedError
  from dtreeviz import utils
label_desc: Use image node_id 4 for calling the ShadowLightGBMTree obj's underlying member method code with example usage: obj.is_classifier() and returns: True, False