How to have multiple actions for multiple agents at the same time using DQN

I work on DQN with 12 agents in my environment. At the same time, each agent has a different state and is supposed to take a different action. But when I try to send all 12 states at the same time to the DQN algorithm, I always get 12 identical actions.

import torch as T
import torch.nn as nn
class DeepQNetwork(nn.Module):
    def __init__(self,lr):
        super(DeepQNetwork, self).__init__()
        self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
        self.to(self.device)
        self.fc1 = nn.Linear(1,64)
        self.fc2 = nn.Linear(64,32)
        self.fc3 = nn.Linear(32,3)

    def forward(self,state):
    x = F.relu(self.fc1(state))
    x = F.relu(self.fc2(x))
    actions = self.fc3(x)
    return actions

the state_ looks like [1,2,3,2,2,3,1,2,3,1,1,3] #It indicates the cluster's number in witch the agent is currently located. an example of expected actions [0,2,1,2,1,0,2,1,1,0,0,2]. #It indicates the cluster's number the agent will be joining.

class agent():
    def __init__(self):
        self.Q_eval = DeepQNetwork(lr)

    def choose_action(self,state_ ):
        if np.random.random() < self.epsilon: 
            action = randomchoice() #a manual function that chooses 12 actions randomly
        else:
            action = []
            state_ = ListToTensor(state_ ) #a manual function to transform the list of states to a tensor 
            state = T.tensor([state_ ]).to(agent.Q_eval.device)
            for i in range(12):
                actions = agent.Q_eval.forward(state[i])
                action.append(T.argmax(actions).item())

I have little knowledge of machine learning so any help or guidance would be helpful to me. Thanks in advance.