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 __init__(self, obs_dim, act_dim):
      super(CartpoleModel, self).__init__()
      hid1_size = 128
      hid2_size = 128
      self.fc1 = nn.Linear(obs_dim, hid1_size)
      self.fc2 = nn.Linear(hid1_size, hid2_size)
      self.fc3 = nn.Linear(hid2_size, act_dim)
  
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 1 to create a new CartpoleModel object from inherited base classes: parl with example: obj = CartpoleModel(obs_dim, act_dim)