code: def forward(self, x: torch.Tensor):
      x = x.requires_grad_(True)
      return self.model(x)
  
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 LinearNet obj's underlying member method code with example usage: obj.forward(x) and returns: self