class_docstr: CNN-layer with multiple kernel-sizes over the word embeddings
class_docstr_tok: ["CNN-layer","with","multiple","kernel-sizes","over","the","word","embeddings"]
code: def load(input_path: str):
      with open(
          os.path.join(input_path, "cnn_config.json"), "r"
      ) as fIn:
          config = json.load(fIn)
  
      weights = torch.load(
          os.path.join(input_path, "pytorch_model.bin"),
          map_location=torch.device("cpu"),
      )
      model = CNN(**config)
      model.load_state_dict(weights)
      return model
  
file_path: sentence-transformers/sentence_transformers/models/CNN.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 CNN obj's underlying member method code with example usage: obj.load(input_path) and returns: model