class_docstr: Fully-connected (s,a)-input Q-function.
  
  Args:
      n_dim_obs (int): Number of dimensions of observation space.
      n_dim_action (int): Number of dimensions of action space.
      n_hidden_channels (int): Number of hidden channels.
      n_hidden_layers (int): Number of hidden layers.
      nonlinearity (callable): Nonlinearity between layers. It must accept a
          Variable as an argument and return a Variable with the same shape.
          Nonlinearities with learnable parameters such as PReLU are not
          supported. It is not used if n_hidden_layers is zero.
      last_wscale (float): Scale of weight initialization of the last layer.
code: def forward(self, state, action):
      h = torch.cat((state, action), dim=1)
      return super().forward(h)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pfrl.initializers import init_lecun_normal
  from pfrl.nn.mlp import MLP
  from pfrl.nn.mlp_bn import MLPBN
  from pfrl.q_function import StateActionQFunction
label_desc: Use image node_id 2 for calling the FCSAQFunction obj's underlying member method code with example usage: obj.forward(state, action) and returns: super