code: def _score(
      self, y_true: np.ndarray, y_pred: np.ndarray, weight=None
  ) -> float:
      if self._is_classifier:
          return accuracy_score(y_true, y_pred, sample_weight=weight)
      else:
          return 1.0 / math.pow(
              2,
              mean_squared_error(y_true, y_pred, sample_weight=weight),
          )
  
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 3 for calling the LinearTrainer obj's underlying member method code with example usage: obj._score(y_true, y_pred, weight) and returns: accuracy_score, unknown