class_docstr: Linear network to solve Cartpole problem.
  
  Args:
      obs_dim (int): Dimension of observation space.
      act_dim (int): Dimension of action space.
code: def forward(self, obs):
      h1 = F.relu(self.fc1(obs))
      h2 = F.relu(self.fc2(h1))
      Q = self.fc3(h2)
      return Q
  
file_path: PARL/examples/DQN/cartpole_model.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import parl
label_desc: Use image node_id 2 for calling the CartpoleModel obj's underlying member method code with example usage: obj.forward(obs) and returns: Q