class_docstr: Wrapper class to apply bias mitigation to any pretrained Model.
  
  # Parameters
  
  vocab : `Vocabulary`
      Vocabulary of base model.
  base_model : `Model`
      Base model for which to mitigate biases.
  bias_mitigator : `Lazy[BiasMitigatorWrapper]`
      Bias mitigator to apply to base model.
class_docstr_tok: ["Wrapper","class","to","apply","bias","mitigation","to","any","pretrained","Model",".","#","Parameters","vocab",":","`","Vocabulary","`","Vocabulary","of","base","model",".","base_model",":","`","Model","`","Base","model","for","which","to","mitigate","biases",".","bias_mitigator",":","`","Lazy","[","BiasMitigatorWrapper","]","`","Bias","mitigator","to","apply","to","base","model","."]
code: def extend_embedder_vocab(self, *args, **kwargs):
      return self.base_model.extend_embedder_vocab(*args, **kwargs)
  
file_path: allennlp/allennlp/fairness/bias_mitigator_applicator.py
imports: from allennlp.fairness.bias_mitigator_wrappers import BiasMitigatorWrapper
  from allennlp.common.lazy import Lazy
  from allennlp.data import Vocabulary
  from allennlp.models import Model
  from allennlp.nn.util import find_embedding_layer
label_desc: Use image node_id 13 for calling the BiasMitigatorApplicator obj's underlying member method code with example usage: obj.extend_embedder_vocab() and returns: self