code: def score(
      self, x: Tensor, y: Tensor, weight: Optional[Tensor] = None
  ) -> float:
      if self._loss_fn is not None:
          return self._loss_fn(y, x).item()
      else:
          raise Exception("mode not trained")
  
imports: import logging
  import math
  import time
  from typing import Optional
  import numpy
  import torch
  from reagent.ope.estimators.types import PredictResults, Trainer, TrainingData
  from sklearn.linear_model import Lasso, LogisticRegression, SGDClassifier
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from torch import Tensor
label_desc: Use image node_id 5 for calling the NNTrainer obj's underlying member method code with example usage: obj.score(x, y, weight) and returns: self