Memory leak when using custom DataGenerator about keras HOT 1 OPEN

def _parse_function(example_proto):
    feature_description = {
            'ny'  : tf.io.FixedLenFeature([], tf.int64, default_value = 0),
            'nx'  : tf.io.FixedLenFeature([], tf.int64, default_value = 0),
            'ntp' : tf.io.FixedLenFeature([], tf.int64, default_value = 0),
            'ntf' : tf.io.FixedLenFeature([], tf.int64, default_value = 0),
            'ncp' : tf.io.FixedLenFeature([], tf.int64, default_value = 0),
            'ncf' : tf.io.FixedLenFeature([], tf.int64, default_value = 0),
            'priors' : tf.io.FixedLenFeature([], tf.string, default_value = ''),
            'forecasts' : tf.io.FixedLenFeature([], tf.string, default_value = ''),
            }
    features = tf.io.parse_example(example_proto, feature_description)
    priors = tf.io.parse_tensor(features['priors'], tf.float32)
    forecasts = tf.io.parse_tensor(features['forecasts'], tf.float32)

    ny  = features['ny']
    nx  = features['nx']
    ntp = features['ntp']
    ntf = features['ntf']
    ncp = features['ncp']
    ncf = features['ncf']

    priors = tf.reshape(priors, shape = [ntp, ny, nx, ncp])
    forecasts = tf.reshape(forecasts, shape = [ntf, ny, nx, ncf])

    return priors, forecasts

...
def create_dataset_onr_tfrecords(path,
                                 glob,
                                 batch_size = 32,
                                 compression = 'GZIP',
                                 shuffle = True,
                                 deterministic = False):
    return tf.data.Dataset.list_files(str(path / glob), shuffle = shuffle).interleave(
            lambda x: tf.data.TFRecordDataset(x, compression_type = compression),
                    cycle_length = tf.data.AUTOTUNE,
                    num_parallel_calls = tf.data.AUTOTUNE,
                    deterministic = deterministic
                    ).map(
                        _parse_function,
                        num_parallel_calls = tf.data.AUTOTUNE
                    ).batch(
                        batch_size, drop_remainder = True
                    ).prefetch(tf.data.AUTOTUNE)