code: def forward(self, x, log=False):
      x = torch.nn.functional.relu(self.conv1(x))
      x = torch.nn.functional.relu(self.conv2(x))
      x = torch.nn.functional.relu(self.conv3(x))
      x = torch.nn.functional.relu(self.conv4(x))
      x = x.view(-1, 2048)
  
      v = self.fc_z_v(torch.nn.functional.relu(self.fc_h_v(x)))
      a = self.fc_z_a(torch.nn.functional.relu(self.fc_h_a(x)))
  
      v, a = v.view(-1, 1, self.atoms), a.view(
          -1, self.action_space, self.atoms
      )
  
      q = v + a - a.mean(1, keepdim=True)
  
      if log:
          q = torch.nn.functional.log_softmax(q, dim=2)
      else:
          q = torch.nn.functional.softmax(q, dim=2)
  
      return q
  
file_path: SerpentAI/serpent/machine_learning/reinforcement_learning/rainbow_dqn/dqn.py
imports: import torch
  from .noisy_linear import NoisyLinear
label_desc: Use image node_id 2 for calling the DQN4 obj's underlying member method code with example usage: obj.forward(x, log) and returns: q