class_docstr: Model wrapper for TextCnn implemented by pytorch.
class_docstr_tok: ["Model","wrapper","for","TextCnn","implemented","by","pytorch","."]
docstr: Unzip sample to input texts and labels.
  
  :param list[Sample] data_samples: list of Samples
  :return: (inputs_text), labels.
docstr_tok: ["Unzip","sample","to","input","texts","and","labels",".",":","param","list","[","Sample","]","data_samples",":","list","of","Samples",":","return",":","(","inputs_text",")",",","labels","."]
code: def unzip_samples(self, data_samples):
      r"""
      Unzip sample to input texts and labels.
  
      :param list[Sample] data_samples: list of Samples
      :return: (inputs_text), labels.
  
      """
      x = []
      y = []
  
      for sample in data_samples:
          x.append(sample["x"])
          y.append(self.label2id[sample["y"]])
  
      return [x], y
  
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 4 for calling the TextCNNTorch obj's underlying member method code with example usage: obj.unzip_samples(data_samples) and returns: list,  y