class_docstr: Plugin reisz function for average treatment effect
class_docstr_tok: ["Plugin","reisz","function","for","average","treatment","effect"]
code: def predict(self, X):
      W = X[:, 1:]  # assume 1-D treatment
      t = X[:, 0]
      preds = self.propmodel.predict_proba(W)
      weights = [
          1 / preds[i, t[i].astype(int)] for i in range(preds.shape[0])
      ]
      weights = np.where(t == 0, -1, 1) * weights
      return weights
  
imports: import numpy
  from econml.grf._base_grf import BaseGRF
  from econml.sklearn_extensions.model_selection import GridSearchCVList
  from econml.utilities import cross_product
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor
  from sklearn.linear_model import Lasso, LogisticRegression
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from dowhy.utils.regression import create_polynomial_function, generate_moment_function
label_desc: Use image node_id 3 for calling the PluginReisz obj's underlying member method code with example usage: obj.predict(X) and returns: weights