docstr: Checks for multiple conditions:
      - method is valid
      - X, tau, and classes are specified
      - model_tau has feature_importances_ attribute after fitting
docstr_tok: ["Checks","for","multiple","conditions",":","-","method","is","valid","-","X",",","tau",",","and","classes","are","specified","-","model_tau","has","feature_importances_","attribute","after","fitting"]
code: def check_conditions(self):
      """
      Checks for multiple conditions:
          - method is valid
          - X, tau, and classes are specified
          - model_tau has feature_importances_ attribute after fitting
      """
      assert (
          self.method in VALID_METHODS
      ), "Current supported methods: {}".format(
          ", ".join(VALID_METHODS)
      )
  
      assert all(
          obj is not None for obj in (self.X, self.tau, self.classes)
      ), "X, tau, and classes must be provided."
  
      model_test = deepcopy(self.model_tau)
      model_test.fit(
          [[0], [1]], [0, 1]
      )  # Fit w/ dummy data to check for feature_importances_ below
      assert hasattr(
          model_test, "feature_importances_"
      ), "model_tau must have the feature_importances_ method (after fitting)"
  
imports: import pandas
  import shap
  import matplotlib.pyplot
  from lightgbm import LGBMRegressor
  from sklearn.inspection import permutation_importance
  from sklearn.model_selection import train_test_split
  from copy import deepcopy
  from causalml.inference.meta.utils import convert_pd_to_np
label_desc: Use image node_id 2 for calling the Explainer obj's underlying member method code with example usage: obj.check_conditions() without return types