code: def __init__(
      self, state_dim, action_dim, hidden_dim, hidden_layers, activation
  ):
      super(EnvironmentModel.Network, self).__init__()
      self._state_dim = state_dim
      self._action_dim = action_dim
      self._hidden_dim = hidden_dim
      self._hidden_layers = hidden_layers
      self._activation = activation
  
      self.layers = []
      dim = self._state_dim + self._action_dim
      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
      # Output is the next state and its reward
      self.layers.append(torch.nn.Linear(dim, self._state_dim + 1))
      self.model = torch.nn.Sequential(*self.layers)
  
imports: import logging
  import gym
  import torch
  from reagent.ope.estimators.sequential_estimators import Action, ActionDistribution, ActionSpace, IPSEstimator, Model, NeuralDualDICE, RandomRLPolicy, RewardProbability, RLEstimatorInput, RLPolicy, State, StateDistribution, Transition
  from reagent.ope.utils import RunningAverage
label_desc: Use image node_id 1 to create a new EnvironmentModel object from inherited base classes: torch.nn with example: obj = EnvironmentModel(state_dim, action_dim, hidden_dim, hidden_layers, activation)