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 train(self, mode: bool = True):
      super().train(mode)
      self.base_model.train(mode)
      # appropriately change requires_grad
      # in bias mitigator and bias direction
      # when train() and eval() are called
      self.bias_mitigator.train(mode)
  
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 2 for calling the BiasMitigatorApplicator obj's underlying member method code with example usage: obj.train(mode) without return types