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, x):
      out = paddle.tanh(self.fc1(x))
      prob = F.softmax(self.fc2(out), axis=-1)
      return prob
  
file_path: PARL/examples/QuickStart/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(x) and returns: prob