code: def load(input_path: str):
      with open(
          os.path.join(input_path, "wordembedding_config.json"), "r"
      ) as fIn:
          config = json.load(fIn)
  
      tokenizer_class = import_from_string(config["tokenizer_class"])
      tokenizer = tokenizer_class.load(input_path)
      weights = torch.load(
          os.path.join(input_path, "pytorch_model.bin"),
          map_location=torch.device("cpu"),
      )
      embedding_weights = weights["emb_layer.weight"]
      model = WordEmbeddings(
          tokenizer=tokenizer,
          embedding_weights=embedding_weights,
          update_embeddings=config["update_embeddings"],
      )
      return model
  
imports: import torch
  from torch import nn
  from typing import List
  import logging
  import gzip
  from tqdm import tqdm
  import numpy
  import os
  import json
  from ..util import import_from_string, fullname, http_get
  from .tokenizer import WordTokenizer, WhitespaceTokenizer
label_desc: Use image node_id 7 for calling the WordEmbeddings obj's underlying member method code with example usage: obj.load(input_path) and returns: model