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 call(self, inputs, training=None, mask=None):
      if len(inputs.get_shape()) != 2:
          raise ValueError(
              f"The rank of inputs of TextCNN must be 2, "
              f"but now is {inputs.get_shape()}"
          )
  
      emb = self.emb_layer(inputs)
      emb = self.drop(emb)
  
      output = self.encoder(emb)
      output = tf.squeeze(output)
  
      # (batch_size, len(self.kernel_sizes)*filters)
      output = self.classifier(output)
  
      return output
  
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 2 for calling the TextCNNTF obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: output