Forward pass of the loss.

        Arguments:
            rewards: Tensor of shape [B, T-1, n_agents]
            actions: Tensor of shape [B, T-1, n_agents]
            terminated: Tensor of shape [B, T-1, n_agents]
            mask: Tensor of shape [B, T-1, n_agents]
            obs: Tensor of shape [B, T, n_agents, obs_size]
            action_mask: Tensor of shape [B, T, n_agents, n_actions]

def forward(self, rewards, actions, terminated, mask, obs, action_mask):
        """Forward pass of the loss.

        Arguments:
            rewards: Tensor of shape [B, T-1, n_agents]
            actions: Tensor of shape [B, T-1, n_agents]
            terminated: Tensor of shape [B, T-1, n_agents]
            mask: Tensor of shape [B, T-1, n_agents]
            obs: Tensor of shape [B, T, n_agents, obs_size]
            action_mask: Tensor of shape [B, T, n_agents, n_actions]
        """

        B, T = obs.size(0), obs.size(1)

        # Calculate estimated Q-Values
        mac_out = []
        h = [s.expand([B, self.n_agents, -1]) for s in self.model.state_init()]
        for t in range(T):
            q, h = _mac(self.model, obs[:, t], h)
            mac_out.append(q)
        mac_out = th.stack(mac_out, dim=1)  # Concat over time

        # Pick the Q-Values for the actions taken -> [B * n_agents, T-1]
        chosen_action_qvals = th.gather(
            mac_out[:, :-1], dim=3, index=actions.unsqueeze(3)).squeeze(3)

        # Calculate the Q-Values necessary for the target
        target_mac_out = []
        target_h = [
            s.expand([B, self.n_agents, -1])
            for s in self.target_model.state_init()
        ]
        for t in range(T):
            target_q, target_h = _mac(self.target_model, obs[:, t], target_h)
            target_mac_out.append(target_q)

        # We don't need the first timesteps Q-Value estimate for targets
        target_mac_out = th.stack(
            target_mac_out[1:], dim=1)  # Concat across time

        # Mask out unavailable actions
        target_mac_out[action_mask[:, 1:] == 0] = -9999999

        # Max over target Q-Values
        if self.double_q:
            # Get actions that maximise live Q (for double q-learning)
            mac_out[action_mask == 0] = -9999999
            cur_max_actions = mac_out[:, 1:].max(dim=3, keepdim=True)[1]
            target_max_qvals = th.gather(target_mac_out, 3,
                                         cur_max_actions).squeeze(3)
        else:
            target_max_qvals = target_mac_out.max(dim=3)[0]

        # Mix
        if self.mixer is not None:
            # TODO(ekl) add support for handling global state? This is just
            # treating the stacked agent obs as the state.
            chosen_action_qvals = self.mixer(chosen_action_qvals, obs[:, :-1])
            target_max_qvals = self.target_mixer(target_max_qvals, obs[:, 1:])

        # Calculate 1-step Q-Learning targets
        targets = rewards + self.gamma * (1 - terminated) * target_max_qvals

        # Td-error
        td_error = (chosen_action_qvals - targets.detach())

        mask = mask.expand_as(td_error)

        # 0-out the targets that came from padded data
        masked_td_error = td_error * mask

        # Normal L2 loss, take mean over actual data
        loss = (masked_td_error**2).sum() / mask.sum()
        return loss, mask, masked_td_error, chosen_action_qvals, targets