StagingError: in user code: IndexError: pop from empty list

[food-bin]

https://deepctr-doc.readthedocs.io/en/latest/Examples.html#multi-value-input-movielens示例中的代码跑出来会出现如上报错
`import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.preprocessing.sequence import pad_sequences

from deepctr.models import DeepFM
from deepctr.feature_column import SparseFeat, VarLenSparseFeat, get_feature_names

def split(x):
key_ans = x.split('|')
for key in key_ans:
if key not in key2index:
# Notice : input value 0 is a special "padding",so we do not use 0 to encode valid feature for sequence input
key2index[key] = len(key2index) + 1
return list(map(lambda x: key2index[x], key_ans))

if name == "main":
data = pd.read_csv("./movielens_sample.txt")
sparse_features = ["movie_id", "user_id",
"gender", "age", "occupation", "zip", ]
target = ['rating']

# 1.Label Encoding for sparse features,and process sequence features
for feat in sparse_features:
    lbe = LabelEncoder()
    data[feat] = lbe.fit_transform(data[feat])
# preprocess the sequence feature

key2index = {}
genres_list = list(map(split, data['genres'].values))
genres_length = np.array(list(map(len, genres_list)))
max_len = max(genres_length)
# Notice : padding=`post`
genres_list = pad_sequences(genres_list, maxlen=max_len, padding='post', )

# 2.count #unique features for each sparse field and generate feature config for sequence feature

fixlen_feature_columns = [SparseFeat(feat, data[feat].max() + 1, embedding_dim=4)
                          for feat in sparse_features]

use_weighted_sequence = False
if use_weighted_sequence:
    varlen_feature_columns = [VarLenSparseFeat(SparseFeat('genres', vocabulary_size=len(
        key2index) + 1, embedding_dim=4), maxlen=max_len, combiner='mean',
                                               weight_name='genres_weight')]  # Notice : value 0 is for padding for sequence input feature
else:
    varlen_feature_columns = [VarLenSparseFeat(SparseFeat('genres', vocabulary_size=len(
        key2index) + 1, embedding_dim=4), maxlen=max_len, combiner='mean',
                                               weight_name=None)]  # Notice : value 0 is for padding for sequence input feature

linear_feature_columns = fixlen_feature_columns + varlen_feature_columns
dnn_feature_columns = fixlen_feature_columns + varlen_feature_columns

feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)

# 3.generate input data for model
model_input = {name: data[name] for name in feature_names}  #
model_input["genres"] = genres_list
model_input["genres_weight"] = np.random.randn(data.shape[0], max_len, 1)

# 4.Define Model,compile and train
model = DeepFM(linear_feature_columns, dnn_feature_columns, task='regression')

model.compile("adam", "mse", metrics=['mse'], )
history = model.fit(model_input, data[target].values,
                    batch_size=256, epochs=10, verbose=2, validation_split=0.2, )`

当我没有使用VarLenSparseFeat的时候，就不会报错

Operating environment(运行环境):

• python version 3.10.13
• tensorflow version 2.12.0
• deepctr version 0.9.3