class_docstr: Bidirectional LSTM running over word embeddings.
class_docstr_tok: ["Bidirectional","LSTM","running","over","word","embeddings","."]
code: def load(input_path: str):
      with open(
          os.path.join(input_path, "lstm_config.json"), "r"
      ) as fIn:
          config = json.load(fIn)
  
      weights = torch.load(
          os.path.join(input_path, "pytorch_model.bin")
      )
      model = LSTM(**config)
      model.load_state_dict(weights)
      return model
  
file_path: sentence-transformers/sentence_transformers/models/LSTM.py
imports: import torch
  from torch import nn
  from typing import List
  import os
  import json
label_desc: Use image node_id 7 for calling the LSTM obj's underlying member method code with example usage: obj.load(input_path) and returns: model