class_docstr: Example:
      >>> #The dataset to detect is y
      >>> y = numpy.array(...)
      >>> ad = AEDetector(roll_len=24)
      >>> ad.fit(y)
      >>> anomaly_scores = ad.score()
      >>> anomaly_indexes = ad.anomaly_indexes()
class_docstr_tok: ["Example",":",">",">",">","#","The","dataset","to","detect","is","y",">",">",">","y","=","numpy.array","(","...",")",">",">",">","ad","=","AEDetector","(","roll_len=24",")",">",">",">","ad.fit","(","y",")",">",">",">","anomaly_scores","=","ad.score","(",")",">",">",">","anomaly_indexes","=","ad.anomaly_indexes","(",")"]
code: def check_rolled(self, arr):
      if arr.size == 0:
          from bigdl.nano.utils.common import invalidInputError
  
          invalidInputError(
              False,
              "rolled array is empty, "
              "please check if roll_len is larger than the total series length",
          )
  
file_path: BigDL/python/chronos/src/bigdl/chronos/detector/anomaly/ae_detector.py
imports: from bigdl.chronos.detector.anomaly.abstract import AnomalyDetector
  from bigdl.chronos.detector.anomaly.util import roll_arr, scale_arr
  import numpy
label_desc: Use image node_id 2 for calling the AEDetector obj's underlying member method code with example usage: obj.check_rolled(arr) without return types