code: def extract_features(
      self, source, get_encoded=False, norm_output=False
  ):
      cpc_feature, encoded, _ = self.forward(source, None)
      if get_encoded:
          cpc_feature = encoded
      if norm_output:
          mean = cpc_feature.mean(dim=1, keepdim=True)
          var = cpc_feature.var(dim=1, keepdim=True)
          cpc_feature = (cpc_feature - mean) / torch.sqrt(var + 1e-08)
      return cpc_feature
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the CPCModel obj's underlying member method code with example usage: obj.extract_features(source, get_encoded, norm_output) and returns: cpc_feature