code: def predict(self, x: Tensor, device=None) -> PredictResults:
      if self._model is not None:
          if hasattr(self._model, "predict_proba"):
              proba = torch.as_tensor(
                  self._model.predict_proba(x),
                  dtype=torch.float,
                  device=device,
              )
              score = (proba * torch.arange(proba.shape[1])).sum(dim=1)
              return PredictResults(
                  torch.argmax(proba, 1), score, proba
              )
          elif hasattr(self._model, "predict"):
              return PredictResults(
                  None,
                  torch.as_tensor(
                      self._model.predict(x),
                      dtype=torch.float,
                      device=device,
                  ),
                  None,
              )
          else:
              raise AttributeError(
                  "model doesn't have predict_proba or predict"
              )
      else:
          raise Exception("model 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 2 for calling the LinearTrainer obj's underlying member method code with example usage: obj.predict(x, device) and returns: PredictResults, PredictResults