code: def __call__(self, obs, batched=False):
      out = (
          obs
          if isinstance(obs, dy.Expression)
          else dy.inputTensor(obs, batched=batched)
      )
  
      for i in range(self.n_layers):
          b, W = dy.parameter(self.bs[i]), dy.parameter(self.Ws[i])
          out = dy.affine_transform([b, W, out])
          if self.layer_norm and i != self.n_layers - 1:
              out = dy.layer_norm(out, self.ln_gs[i], self.ln_bs[i])
          if self.specified_activation:
              if self.activation[i] is not None:
                  out = self.activation[i](out)
          else:
              out = self.activation(out)
      return out
  
file_path: dynet/examples/reinforcement-learning/network.py
imports: import dynet
label_desc: Use image node_id 2 for calling the MLP obj's underlying member method code with example usage: obj.__call__(obs, batched) and returns: out