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 __init__(
      self,
      *,
      reisz_functions,
      moment_function=generate_moment_function,
      l2_regularizer=1e-3,
      n_estimators=100,
      criterion="mse",
      max_depth=None,
      min_samples_split=10,
      min_samples_leaf=5,
      min_weight_fraction_leaf=0.0,
      min_var_fraction_leaf=None,
      min_var_leaf_on_val=False,
      max_features="auto",
      min_impurity_decrease=0.0,
      max_samples=0.45,
      min_balancedness_tol=0.45,
      honest=True,
      inference=True,
      fit_intercept=True,
      subforest_size=4,
      n_jobs=-1,
      random_state=None,
      verbose=0,
      warm_start=False,
  ):
      self.reisz_functions = reisz_functions
      self.moment_function = moment_function
      self.l2_regularizer = l2_regularizer
      self.num_reisz_functions = len(self.reisz_functions)
  
      super().__init__(
          n_estimators=n_estimators,
          criterion=criterion,
          max_depth=max_depth,
          min_samples_split=min_samples_split,
          min_samples_leaf=min_samples_leaf,
          min_weight_fraction_leaf=min_weight_fraction_leaf,
          min_var_fraction_leaf=min_var_fraction_leaf,
          min_var_leaf_on_val=min_var_leaf_on_val,
          max_features=max_features,
          min_impurity_decrease=min_impurity_decrease,
          max_samples=max_samples,
          min_balancedness_tol=min_balancedness_tol,
          honest=honest,
          inference=inference,
          fit_intercept=False,
          subforest_size=subforest_size,
          n_jobs=n_jobs,
          random_state=random_state,
          verbose=verbose,
          warm_start=warm_start,
      )
  
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 1 to create a new ReiszRepresenter object from inherited base classes: BaseGRF with example: obj = ReiszRepresenter()