code: def run(self, x):
      features = []
  
      h = x
  
      for f in range(max(self.feature_layers) + 1):
          h = self.model.features[f](h)
          if f in self.feature_layers:
              not_normed_features = h.clone().view(h.size(0), -1)
              features.append(not_normed_features)
  
      return torch.cat(features, dim=1)
  
file_path: BigDL/python/nano/client-application/VideoStylization/feature_model.py
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  from torchvision import models
label_desc: Use image node_id 3 for calling the FeaturesNet obj's underlying member method code with example usage: obj.run(x) and returns: torch