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 forward(self, features):
      token_embeddings = features["token_embeddings"]
  
      token_embeddings = token_embeddings.transpose(1, -1)
      vectors = [conv(token_embeddings) for conv in self.convs]
      out = torch.cat(vectors, 1).transpose(1, -1)
  
      features.update({"token_embeddings": out})
      return features
  
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 2 for calling the CNN obj's underlying member method code with example usage: obj.forward(features) and returns: features