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 __init__(self, model, output_layer_name):
      super(FeatureExtractorBackbone, self).__init__()
      self.model = model
      self.output_layer_name = output_layer_name
      self.output = None  # this will store the layer output
      self.add_hooks(self.model)
  
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 1 to create a new FeatureExtractorBackbone object from inherited base classes: nn with example: obj = FeatureExtractorBackbone(model, output_layer_name)