code: def forward(self, agent_qs, states):
          
          bs = agent_qs.size(0)
          states = states.reshape(-1, self.state_dim)
          agent_qs = agent_qs.view(-1, 1, self.n_agents)
          # First layer
          w1 = th.abs(self.hyper_w_1(states))
          b1 = self.hyper_b_1(states)
          w1 = w1.view(-1, self.n_agents, self.embed_dim)
          b1 = b1.view(-1, 1, self.embed_dim)
          hidden = F.elu(th.bmm(agent_qs, w1) + b1)
          # Second layer
          w_final = th.abs(self.hyper_w_final(states))
          w_final = w_final.view(-1, self.embed_dim, 1)
          # State-dependent bias
          v = self.V(states).view(-1, 1, 1)
          # Compute final output
          y = th.bmm(hidden, w_final) + v
          # Reshape and return
          q_tot = y.view(bs, -1, 1)
          return q_tot
docs: Forward pass for the mixer.
  
          Arguments:
              agent_qs: Tensor of shape [B, T, n_agents, n_actions]
              states: Tensor of shape [B, T, state_dim]