old_contents: import paddle.fluid as fluid
  import math
  
  dense_feature_dim = 13
  
  def ctr_dnn_model(embedding_size, sparse_feature_dim):
      dense_input = fluid.layers.data(
          name="dense_input", shape=[dense_feature_dim], dtype='float32')
      sparse_input_ids = [
          fluid.layers.data(
              name="C" + str(i), shape=[1], lod_level=1, dtype='int64')
          for i in range(1, 27)
      ]
  
      def embedding_layer(input):
          return fluid.layers.embedding(
              input=input,
              is_sparse=True,
              is_distributed=True,
              size=[sparse_feature_dim, embedding_size],
              param_attr=fluid.ParamAttr(name="SparseFeatFactors", initializer=fluid.initializer.Uniform()))
  
      sparse_embed_seq = map(embedding_layer, sparse_input_ids)
      concated = fluid.layers.concat(sparse_embed_seq + [dense_input], axis=1)
  
      fc1 = fluid.layers.fc(input=concated, size=400, act='relu',
          param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1/math.sqrt(concated.shape[1]))))
      fc2 = fluid.layers.fc(input=fc1, size=400, act='relu',
          param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1/math.sqrt(fc1.shape[1]))))
      fc3 = fluid.layers.fc(input=fc2, size=400, act='relu',
          param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1/math.sqrt(fc2.shape[1]))))
      predict = fluid.layers.fc(input=fc3, size=2, act='softmax',
          param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1/math.sqrt(fc3.shape[1]))))
  
      label = fluid.layers.data(name='label', shape=[1], dtype='int64')
  
      data_list = [dense_input] + sparse_input_ids + [label]
  
      cost = fluid.layers.cross_entropy(input=predict, label=label)
      avg_cost = fluid.layers.reduce_sum(cost)
      accuracy = fluid.layers.accuracy(input=predict, label=label)
      auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict, label=label, num_thresholds=2**12, slide_steps=20)
  
      return avg_cost, data_list, auc_var, batch_auc_var
  
new_contents: import paddle.fluid as fluid
  import math
  
  dense_feature_dim = 13
  
  def ctr_dnn_model(embedding_size, sparse_feature_dim):
      dense_input = fluid.layers.data(
          name="dense_input", shape=[dense_feature_dim], dtype='float32')
      sparse_input_ids = [
          fluid.layers.data(
              name="C" + str(i), shape=[1], lod_level=1, dtype='int64')
          for i in range(1, 27)
      ]
  
      def embedding_layer(input):
          return fluid.layers.embedding(
              input=input,
              is_sparse=True,
              # you need to patch https://github.com/PaddlePaddle/Paddle/pull/14190
              # if you want to set is_distributed to True
              is_distributed=False,
              size=[sparse_feature_dim, embedding_size],
              param_attr=fluid.ParamAttr(name="SparseFeatFactors", initializer=fluid.initializer.Uniform()))
  
      sparse_embed_seq = map(embedding_layer, sparse_input_ids)
      concated = fluid.layers.concat(sparse_embed_seq + [dense_input], axis=1)
  
      fc1 = fluid.layers.fc(input=concated, size=400, act='relu',
          param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1/math.sqrt(concated.shape[1]))))
      fc2 = fluid.layers.fc(input=fc1, size=400, act='relu',
          param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1/math.sqrt(fc1.shape[1]))))
      fc3 = fluid.layers.fc(input=fc2, size=400, act='relu',
          param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1/math.sqrt(fc2.shape[1]))))
      predict = fluid.layers.fc(input=fc3, size=2, act='softmax',
          param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1/math.sqrt(fc3.shape[1]))))
  
      label = fluid.layers.data(name='label', shape=[1], dtype='int64')
  
      data_list = [dense_input] + sparse_input_ids + [label]
  
      cost = fluid.layers.cross_entropy(input=predict, label=label)
      avg_cost = fluid.layers.reduce_sum(cost)
      accuracy = fluid.layers.accuracy(input=predict, label=label)
      auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict, label=label, num_thresholds=2**12, slide_steps=20)
  
      return avg_cost, data_list, auc_var, batch_auc_var
  
subject: set is_distributed to false