code: def forward(
      self, x, with_latent=False, fake_relu=False, no_relu=False
  ):
      x = self.features(x)
      x_latent = self.classifier[:2](x)
      x_relu = (
          self.last_relu(x_latent)
          if not fake_relu
          else self.classifier_last_relu_fake(x_latent)
      )
      x_out = self.classifier[-1:](x_relu)
      x_out = torch.flatten(x_out, 1)
  
      if with_latent and no_relu:
          return (
              x_out,
              x_latent,
          )  # potentially will need to flatten x_latent
      if with_latent:
          return (
              x_out,
              x_relu,
          )  # potentially will need to flatten x_relu
      return x_out
  
file_path: robustness/robustness/imagenet_models/squeezenet.py
imports: import torch
  import torch.nn
  import torch.nn.init
  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 SqueezeNet 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