code: def _forward_pooled(self, x):
      assert x.size(1) == self.max_seq_length
  
      x = self.embedding(x)
      x = x.unsqueeze(dim=1)
  
      out_tensors = []
      for conv, pool in zip(self.convs, self.pools):
          activation = pool(F.relu(conv(x)))
          out_tensors.append(activation)
  
      x = torch.cat(out_tensors, dim=1)
      return x.view(x.size(0), -1)
  
imports: from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
label_desc: Use image node_id 3 for calling the KimCNN obj's underlying member method code with example usage: obj._forward_pooled(x) and returns: x