code: def simpleNet(
      seq_length, vocab_size, embedding_dim, num_cla, kernelNum=128
  ):
      inputX = keras.layers.Input(shape=(seq_length,), dtype="int32")
      embOut = keras.layers.Embedding(
          vocab_size, embedding_dim, input_length=seq_length
      )(inputX)
      # 使用长度为5的词窗去执行卷积
      conv1 = keras.layers.Conv1D(
          kernelNum, 5, padding="same", strides=1
      )(embOut)
      maxp1 = keras.layers.MaxPool1D(pool_size=int(conv1.shape[1]))(
          conv1
      )
      flat = keras.layers.Flatten()(maxp1)
      dense1 = keras.layers.Dense(128)(flat)
      drop = keras.layers.Dropout(0.25)(dense1)
      denseRelu = keras.layers.ReLU()(drop)
      predictY = keras.layers.Dense(num_cla, activation="softmax")(
          denseRelu
      )
      # 编译模型
      model = keras.models.Model(inputs=inputX, outputs=predictY)
      # 指定loss的计算方法，设置优化器，编译模型
      model.compile(
          optimizer=keras.optimizers.Adam(lr=1e-3),
          loss="categorical_crossentropy",
          metrics=["accuracy"],
      )
      return model
  
label_desc: Use image node_id 3 for calling a global function with example usage: simpleNet(seq_length, vocab_size, embedding_dim, num_cla, kernelNum) and returns: model