code: def forward(self, x):
      x = x.unsqueeze(1)  # (batch, Ci, len, d)
      x = [
          F.relu(conv(x)).squeeze(3) for conv in self.convs1
      ]  # [(batch, Co, len), ...]
      x = [
          F.max_pool1d(i, i.size(2)).squeeze(2) for i in x
      ]  # [(N,Co), ...]
      x = torch.cat(x, 1)
      return x
  
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 2 for calling the CNNTextLayer obj's underlying member method code with example usage: obj.forward(x) and returns: x