class_docstr: Model wrapper for TextCnn implemented by pytorch.
class_docstr_tok: ["Model","wrapper","for","TextCnn","implemented","by","pytorch","."]
code: def __init__(self):
      glove_embedding = GloveEmbedding()
      word2id = glove_embedding.word2id
  
      super().__init__(
          model=TextCNNTorchModel(
              init_embedding=glove_embedding.embedding
          ),
          task="SA",
          tokenizer=GloveTokenizer(
              word_id_map=word2id,
              unk_token_id=glove_embedding.oovid,
              pad_token_id=glove_embedding.padid,
              max_length=30,
          ),
      )
      self.label2id = {"positive": 0, "negative": 1}
  
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 1 to create a new TextCNNTorch object from inherited base classes: TorchModel with example: obj = TextCNNTorch()