class_docstr: Model wrapper for TextCnn implemented by pytorch.
class_docstr_tok: ["Model","wrapper","for","TextCnn","implemented","by","pytorch","."]
docstr: Tokenize text, convert tokens to id and run the model.
  
  :param batch_texts: (batch_size,) batch text input
  :return: numpy.array()
docstr_tok: ["Tokenize","text",",","convert","tokens","to","id","and","run","the","model",".",":","param","batch_texts",":","(","batch_size",",",")","batch","text","input",":","return",":","numpy.array","(",")"]
code: def __call__(self, batch_texts):
      r"""
      Tokenize text, convert tokens to id and run the model.
  
      :param batch_texts: (batch_size,) batch text input
      :return: numpy.array()
  
      """
      model_device = next(self.model.parameters()).device
      inputs_ids = [
          self.encode(batch_text) for batch_text in batch_texts
      ]
      ids = torch.tensor(inputs_ids).to(model_device)
  
      return self.model(ids).detach().cpu().numpy()
  
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 2 for calling the TextCNNTorch obj's underlying member method code with example usage: obj.__call__(batch_texts) and returns: self