class_docstr: This PyTorch module allows us to extract features from a backbone network
  given a layer name.
class_docstr_tok: ["This","PyTorch","module","allows","us","to","extract","features","from","a","backbone","network","given","a","layer","name","."]
code: def get_activation(self):
      def hook(model, input, output):
          self.output = output.detach()
  
      return hook
  
imports: from avalanche.benchmarks.utils import _make_taskaware_classification_dataset
  from avalanche.models.dynamic_modules import MultiTaskModule, DynamicModule
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  from collections import OrderedDict
  from avalanche.benchmarks.scenarios import CLExperience
label_desc: Use image node_id 4 for calling the FeatureExtractorBackbone obj's underlying member method code with example usage: obj.get_activation() and returns: hook