docstr: Parameters
  ----------
  x : torch.LongTensor or torch.cuda.LongTensor
      input tensor (batch_size, max_sequence_length) with padded sequences of word ids
docstr_tok: ["Parameters","--","--","--","--","--","x",":","torch.LongTensor","or","torch.cuda.LongTensor","input","tensor","(","batch_size",",","max_sequence_length",")","with","padded","sequences","of","word","ids"]
code: def forward(self, x):
      """
      Parameters
      ----------
      x : torch.LongTensor or torch.cuda.LongTensor
          input tensor (batch_size, max_sequence_length) with padded sequences of word ids
      """
      x = self._forward_pooled(x)
      return self._dropout_and_fc(x)
  
imports: from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
label_desc: Use image node_id 2 for calling the KimCNN obj's underlying member method code with example usage: obj.forward(x) and returns: self