class_docstr: A convolutional neural network for text classification.
  
  We use different versions of this network to pretrain models for
  text classification.
code: def save_pretrained(self, output_path):
      if not os.path.exists(output_path):
          os.makedirs(output_path)
      state_dict = {k: v.cpu() for k, v in self.state_dict().items()}
      torch.save(
          state_dict, os.path.join(output_path, "pytorch_model.bin")
      )
      with open(os.path.join(output_path, "config.json"), "w") as f:
          json.dump(self._config, f)
  
imports: import json
  import os
  import torch
  from torch import nn
  from torch.nn import functional
  import textattack
  from textattack.model_args import TEXTATTACK_MODELS
  from textattack.models.helpers import GloveEmbeddingLayer
  from textattack.models.helpers.utils import load_cached_state_dict
  from textattack.shared import utils
label_desc: Use image node_id 3 for calling the WordCNNForClassification obj's underlying member method code with example usage: obj.save_pretrained(output_path) without return types