class_docstr: Implements a continuous MLP Q-value network.
  
  It predicts the Q-value for all actions based on the input state. It uses
  a PyTorch neural network module to fit the function of Q(s, a).
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docstr: Return Q-value(s).
  
  Args:
      observations (np.ndarray): observations.
      actions (np.ndarray): actions.
  
  Returns:
      torch.Tensor: Output value
docstr_tok: ["Return","Q-value","(","s",")",".","Args",":","observations","(","np.ndarray",")",":","observations",".","actions","(","np.ndarray",")",":","actions",".","Returns",":","torch.Tensor",":","Output","value"]
code: def forward(self, observations, actions):
      """Return Q-value(s).
  
      Args:
          observations (np.ndarray): observations.
          actions (np.ndarray): actions.
  
      Returns:
          torch.Tensor: Output value
      """
      return super().forward(torch.cat([observations, actions], 1))
  
imports: import torch
  from garage.torch.modules import MLPModule
label_desc: Use image node_id 2 for calling the ContinuousMLPQFunction obj's underlying member method code with example usage: obj.forward(observations, actions) and returns: super