docstr: Unfreeze weights of embedding, decoder, and classification layers.
          
docstr_tok: ["Unfreeze","weights","of","embedding",",","decoder",",","and","classification","layers","."]
code: def unfreeze(self) -> None:
      """Unfreeze weights of embedding, decoder, and classification layers."""
      for model_num in range(self.num_models):
          for param in self.embeddings[model_num].parameters():
              param.requires_grad = True
          self.embeddings[model_num].train()
          for param in self.decoders[model_num].parameters():
              param.requires_grad = True
          self.decoders[model_num].train()
          for param in self.log_softmaxes[model_num].parameters():
              param.requires_grad = True
          self.log_softmaxes[model_num].train()
          for param in self.encoders[model_num].parameters():
              param.requires_grad = True
          self.encoders[model_num].train()
  
imports: from contextlib import contextmanager
  import torch
  from nemo.collections.common.parts import NEG_INF, mask_padded_tokens
label_desc: Use image node_id 11 for calling the EnsembleBeamSearchSequenceGenerator obj's underlying member method code with example usage: obj.unfreeze() without return types