class_docstr: Generalized Random Forest to estimate Reisz Representer (RR)
  See: https://github.com/microsoft/EconML/blob/main/econml/grf/_base_grf.py
  :param reisz_functions: List of polynomial functions of n degree to approximate reisz representer created using create_polynomial_function
  :param moment_function: moment function m(W,g) whose expected value is used to calculate estimate
  :param l2_regularizer: l2 penalty while modeling (default = 1e-3)
  For tuning other parameters see https://econml.azurewebsites.net/_autosummary/econml.grf.CausalForest.html
class_docstr_tok: ["Generalized","Random","Forest","to","estimate","Reisz","Representer","(","RR",")","See",":","https",":","\/\/github.com\/microsoft\/EconML\/blob\/main\/econml\/grf\/_base_grf.py",":","param","reisz_functions",":","List","of","polynomial","functions","of","n","degree","to","approximate","reisz","representer","created","using","create_polynomial_function",":","param","moment_function",":","moment","function","m","(","W",",","g",")","whose","expected","value","is","used","to","calculate","estimate",":","param","l2_regularizer",":","l2","penalty","while","modeling","(","default","=","1e-3",")","For","tuning","other","parameters","see","https",":","\/\/econml.azurewebsites.net\/_autosummary\/econml.grf.CausalForest.html"]
code: def _get_alpha_and_pointJ(self, X, T, y):
      n_riesz_feats = len(self.reisz_functions)
      TX = np.hstack([T, X])
      riesz_feats = np.hstack(
          [feat_fn(TX) for feat_fn in self.reisz_functions]
      )
      mfeats = np.hstack(
          [
              self.moment_function(TX, feat_fn)
              for feat_fn in self.reisz_functions
          ]
      )
      alpha = np.zeros((X.shape[0], n_riesz_feats))
      alpha[:, :n_riesz_feats] = mfeats
      riesz_cov_matrix = cross_product(
          riesz_feats, riesz_feats
      ).reshape((X.shape[0], n_riesz_feats, n_riesz_feats))
      penalty = self.l2_regularizer * np.eye(n_riesz_feats)
      penalty[0, 0] = 0
      pointJ = riesz_cov_matrix + penalty
      return alpha, pointJ.reshape((X.shape[0], -1))
  
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 2 for calling the ReiszRepresenter obj's underlying member method code with example usage: obj._get_alpha_and_pointJ(X, T, y) and returns: alpha,  pointJ