code: def __init__(
      self,
      D_in: int,
      H: int,
      D_out: int,
      hidden_layers: int = 2,
      activation=torch.nn.ReLU,
  ) -> None:
      super(LinearNet, self).__init__()
      self._hidden_dim = H
      self._hidden_layers = hidden_layers
      self._activation = activation
      self._out_dim = D_out
  
      self.layers = []
      dim = D_in
      for _ in range(self._hidden_layers):
          self.layers.append(torch.nn.Linear(dim, self._hidden_dim))
          self.layers.append(self._activation())
          dim = self._hidden_dim
      self.layers.append(torch.nn.Linear(dim, self._out_dim))
      self.model = torch.nn.Sequential(*self.layers)
  
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 1 to create a new LinearNet object from inherited base classes: torch.nn with example: obj = LinearNet(D_in, H, D_out, hidden_layers, activation)