code: def forward(
      self, x, with_latent=False, fake_relu=False, no_relu=False
  ):
      x = self.features(x)
      x = self.avgpool(x)
      x = x.view(x.size(0), 256 * 6 * 6)
      x_latent = self.classifier(x)
      x_relu = (
          self.last_relu_fake(x_latent)
          if fake_relu
          else self.last_relu(x_latent)
      )
      x_out = self.last_layer(x_relu)
  
      if with_latent and no_relu:
          return x_out, x_latent
      if with_latent:
          return x_out, x_relu
      return x_out
  
file_path: robustness/robustness/imagenet_models/alexnet.py
imports: import torch.nn
  from torch.hub import load_state_dict_from_url
  from ..tools.custom_modules import FakeReLUM
label_desc: Use image node_id 2 for calling the AlexNet obj's underlying member method code with example usage: obj.forward(x, with_latent, fake_relu, no_relu) and returns: x_out, x_out,  x_latent, x_out,  x_relu