rebatch api produce an Check failed: limit <= dim0_size error about hybridbackend HOT 2 CLOSED

import numpy as np
import pandas as pd
import random

data_list = []
for i in range(1, 10000):
    int_feature = random.randint(1, 100)
    # float_feature = random.random()
    array_feature = [random.randint(1, 10) for x in range(0, 4)]
    data_list.append([int_feature, array_feature])

df = pd.DataFrame(data_list, columns=["int_feature", "array_feature"])
df.to_parquet("parquet_sample_file.parquet")

filenames_ds = tf.data.Dataset.from_tensor_slices(["parquet_sample_file.parquet"])

hb_fields = []
hb_fields.append(hb.data.DataFrame.Field("int_feature", tf.int64, ragged_rank=0))
# hb_fields.append(hb.data.DataFrame.Field("float_feature", tf.float32, ragged_rank=0))
hb_fields.append(hb.data.DataFrame.Field("array_feature", tf.int64, ragged_rank=1))

iterator = filenames_ds.apply(hb.data.read_parquet(100, hb_fields, num_parallel_reads=tf.data.experimental.AUTOTUNE))
iterator = iterator.apply(hb.data.rebatch(100, fields=hb_fields)).repeat(30)

iterator = iterator.make_one_shot_iterator()
item = iterator.get_next()
with tf.Session() as sess:
    print("======  start ======")
    total_batch_size = 0
    while True:
        try:
            a = sess.run(item)
        except tf.errors.OutOfRangeError:
            break