class_docstr: Model wrapper for TextCnn implemented by pytorch.
class_docstr_tok: ["Model","wrapper","for","TextCnn","implemented","by","pytorch","."]
docstr: Tokenize inputs and convert it to ids.
  
  :param inputs: model original input
  :return: list of inputs ids
docstr_tok: ["Tokenize","inputs","and","convert","it","to","ids",".",":","param","inputs",":","model","original","input",":","return",":","list","of","inputs","ids"]
code: def encode(self, inputs):
      r"""
      Tokenize inputs and convert it to ids.
  
      :param inputs: model original input
      :return: list of inputs ids
  
      """
      return self.tokenizer.encode(inputs)
  
file_path: textflint/textflint/input/model/flint_model/textcnn_torch.py
imports: import torch
  from .torch_model import TorchModel
  from ..test_model.textcnn_torch_model import TextCNNTorchModel
  from ..test_model.glove_embedding import GloveEmbedding
  from ..tokenizers.glove_tokenizer import GloveTokenizer
label_desc: Use image node_id 3 for calling the TextCNNTorch obj's underlying member method code with example usage: obj.encode(inputs) and returns: self