class_docstr: Token embeddings are weighted mean of their different hidden layer representations
class_docstr_tok: ["Token","embeddings","are","weighted","mean","of","their","different","hidden","layer","representations"]
code: def save(self, output_path):
      with open(os.path.join(output_path, "config.json"), "w") as fOut:
          json.dump(self.get_config_dict(), fOut, indent=2)
  
      torch.save(
          self.state_dict(),
          os.path.join(output_path, "pytorch_model.bin"),
      )
  
file_path: sentence-transformers/sentence_transformers/models/WeightedLayerPooling.py
imports: import torch
  from torch import Tensor
  from torch import nn
  from typing import Dict
  import os
  import json
label_desc: Use image node_id 5 for calling the WeightedLayerPooling obj's underlying member method code with example usage: obj.save(output_path) without return types