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","."]
docstr: :param model:
  :param outputs: Outputs from layers specified in `output_layer_names`
  will be stored in `output` variable
  :param output_layer_names:
  :return:
docstr_tok: [":","param","model",":",":","param","outputs",":","Outputs","from","layers","specified","in","`","output_layer_names","`","will","be","stored","in","`","output","`","variable",":","param","output_layer_names",":",":","return",":"]
code: def add_hooks(self, model):
      """
      :param model:
      :param outputs: Outputs from layers specified in `output_layer_names`
      will be stored in `output` variable
      :param output_layer_names:
      :return:
      """
      name_to_module = self.get_name_to_module(model)
      name_to_module[self.output_layer_name].register_forward_hook(
          self.get_activation()
      )
  
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 5 for calling the FeatureExtractorBackbone obj's underlying member method code with example usage: obj.add_hooks(model) without return types