code: def forward(
      self, x, with_latent=False, fake_relu=False, no_relu=False
  ):
      assert (
          not no_relu
      ), "DenseNet has no pre-ReLU activations, no_relu not supported"
      out = self.conv1(x)
      out = self.trans1(self.dense1(out))
      out = self.trans2(self.dense2(out))
      out = self.trans3(self.dense3(out))
      out = self.dense4(out)
      if fake_relu:
          out = F.avg_pool2d(FakeReLU.apply(self.bn(out)), 4)
      else:
          out = F.avg_pool2d(F.relu(self.bn(out)), 4)
      out = out.view(out.size(0), -1)
      latent = out.clone()
      out = self.linear(out)
      if with_latent:
          return out, latent
      return out
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from ..tools.custom_modules import FakeReLU
label_desc: Use image node_id 3 for calling the DenseNet obj's underlying member method code with example usage: obj.forward(x, with_latent, fake_relu, no_relu) and returns: out, out,  latent