class_docstr: A convolutional neural network for text classification which
  implemented by tensorflow.
  
  We use different versions of this network to pretrain models for
  text classification.
class_docstr_tok: ["A","convolutional","neural","network","for","text","classification","which","implemented","by","tensorflow",".","We","use","different","versions","of","this","network","to","pretrain","models","for","text","classification","."]
code: def __init__(
      self,
      init_embedding=None,
      hidden_size=100,
      dropout=0.2,
      num_labels=2,
      seq_length=30,
  ):
      super(TextCNNTF, self).__init__()
      self.num_labels = num_labels
  
      glove_embedding = GloveEmbedding()
      self.emb_layer = Embedding(
          input_dim=glove_embedding.vocab_size,
          output_dim=glove_embedding.embedding_size,
          weights=[init_embedding],
      )
      self.drop = Dropout(dropout)
      self.encoder = CNNTextLayer(
          seq_length=seq_length, widths=[3, 4, 5], filters=hidden_size
      )
  
      self.classifier = Dense(num_labels, activation="softmax")
  
file_path: textflint/textflint/input/model/test_model/textcnn_tf_model.py
imports: import tensorflow
  from tensorflow.keras.layers import Embedding, Conv1D, Dense, MaxPooling1D, Dropout
  from tensorflow.keras import Model
  from .glove_embedding import GloveEmbedding
label_desc: Use image node_id 1 to create a new TextCNNTF object from inherited base classes: Model with example: obj = TextCNNTF(init_embedding, hidden_size, dropout, num_labels, seq_length)