class_docstr: Fully-connected + BN (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.
      normalize_input (bool): If set to True, Batch Normalization is applied
          to both observations and actions.
      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.
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code: def __call__(self, state, action):
      h = F.concat((state, action), axis=1)
      return super().__call__(h)
  
imports: import chainer
  from chainer import functions
  from chainer.initializers import LeCunNormal
  from chainer import links
  from chainerrl.links.mlp import MLP
  from chainerrl.links.mlp_bn import MLPBN
  from chainerrl.q_function import StateActionQFunction
  from chainerrl.recurrent import RecurrentChainMixin
label_desc: Use image node_id 2 for calling the FCBNSAQFunction obj's underlying member method code with example usage: obj.__call__(state, action) and returns: super