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):
          """Initialize parameters and build model.
          Params
          ======
              state_size (int): Dimension of each state
              action_size (int): Dimension of each action
              seed (int): Random seed
          """
          super(QNetwork, self).__init__()
          self.seed = torch.manual_seed(seed)
          self.input_dim = state_size
          self.output_dim = action_size
          "*** YOUR CODE HERE ***"
  
          self.feature_layer = nn.Sequential(
              nn.Linear(self.input_dim, 32),
              nn.ReLU(),
              nn.Linear(32, 32),
              nn.ReLU()
          )
  
          self.value_stream = nn.Sequential(
              nn.Linear(32, 32),
              nn.ReLU(),
              nn.Linear(32,1)
          )
  
          self.advantage_stream = nn.Sequential(
              nn.Linear(32,32),
              nn.ReLU(),
              nn.Linear(32, self.output_dim)
          )
  
      def forward(self, state):
          """Build a network that maps state -> action values."""
          features = self.feature_layer(state)
          values = self.value_stream(features)
          advantages = self.advantage_stream(features)
          qvals = values + (advantages - advantages.mean())
          return qvals