text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  class QNetwork(nn.Module):
      """Actor (Policy) Model."""
  
      def __init__(self, state_size, action_size, seed, fc1_units=64, fc2_units=64, fc3v_units=32, fc3a_units=32):
          """Initialize parameters and build model.
          Params
          ======
              state_size (int): Dimension of each state
              action_size (int): Dimension of each action
              seed (int): Random seed
              fc1_units (int): Number of nodes in first hidden layer
              fc2_units (int): Number of nodes in second hidden layer
          """
          super(QNetwork, self).__init__()
          self.seed = torch.manual_seed(seed)
          self.fc1 = nn.Linear(state_size, fc1_units)
          self.fc2 = nn.Linear(fc1_units, fc2_units)
          self.fc3v = nn.Linear(fc2_units, fc3v_units)
          self.fc3a = nn.Linear(fc2_units, fc3a_units)
          self.fc4v = nn.Linear(fc3v_units, 1)
          self.fc4a = nn.Linear(fc3a_units, action_size)
  
      def forward(self, state):
          """Build a network that maps state -> action values."""
          x = F.relu(self.fc1(state))
          x = F.relu(self.fc2(x))
          xv = F.relu(self.fc3v(x))
          v = self.fc4v(xv)
          xa = F.relu(self.fc3v(x))
          a = self.fc4a(xa)
          q = a + (v - a.mean(1, True))
          return v, a, q
  