code: def reisz_scoring(g, X):
      """
      Returns loss for a estimator model
      :param g: model to be evaluated
      :param X: data for testing the model
  
      :returns: floating point number that quantifies the model prediction
      Loss(g) = E[2*m(X,g) - g(X)**2]
      """
      loss = np.mean(
          2 * generate_moment_function(W=X, g=g.predict)
          - g.predict(X) ** 2
      )
      return loss
  
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 a global function with example usage: reisz_scoring(g, X) and returns: loss