class_docstr: The anomaly detector model for sequence data based on LSTM.
  
  # Arguments
  feature_shape: The input shape of features, including unroll_length and feature_size.
  hidden_layers: Units of hidden layers of LSTM.
  dropouts:     Fraction of the input units to drop out. Float between 0 and 1.
class_docstr_tok: ["The","anomaly","detector","model","for","sequence","data","based","on","LSTM",".","#","Arguments","feature_shape",":","The","input","shape","of","features",",","including","unroll_length","and","feature_size",".","hidden_layers",":","Units","of","hidden","layers","of","LSTM",".","dropouts",":","Fraction","of","the","input","units","to","drop","out",".","Float","between","0","and","1","."]
docstr: Unroll a rdd of arrays to prepare features and labels.
  
  # Arguments
  data_rdd: RDD[Array]. data to be unrolled, it holds original time series features
  unroll_length: Int. the length of precious values to predict future value.
  predict_step: Int. How many time steps to predict future value, default is 1.
  return: an rdd of FeatureLableIndex
  a simple example
               data: (1,2,3,4,5,6); unrollLength: 2, predictStep: 1
               features, label, index
               (1,2), 3, 0
               (2,3), 4, 1
               (3,4), 5, 2
               (4,5), 6, 3
docstr_tok: ["Unroll","a","rdd","of","arrays","to","prepare","features","and","labels",".","#","Arguments","data_rdd",":","RDD","[","Array","]",".","data","to","be","unrolled",",","it","holds","original","time","series","features","unroll_length",":","Int",".","the","length","of","precious","values","to","predict","future","value",".","predict_step",":","Int",".","How","many","time","steps","to","predict","future","value",",","default","is","1.","return",":","an","rdd","of","FeatureLableIndex","a","simple","example","data",":","(","1,2,3,4,5,6",")",";","unrollLength",":","2",",","predictStep",":","1","features",",","label",",","index","(","1,2",")",",","3",",","0","(","2,3",")",",","4",",","1","(","3,4",")",",","5",",","2","(","4,5",")",",","6",",","3"]
code: def unroll(cls, data_rdd, unroll_length, predict_step=1):
      """
      Unroll a rdd of arrays to prepare features and labels.
  
      # Arguments
      data_rdd: RDD[Array]. data to be unrolled, it holds original time series features
      unroll_length: Int. the length of precious values to predict future value.
      predict_step: Int. How many time steps to predict future value, default is 1.
      return: an rdd of FeatureLableIndex
      a simple example
                   data: (1,2,3,4,5,6); unrollLength: 2, predictStep: 1
                   features, label, index
                   (1,2), 3, 0
                   (2,3), 4, 1
                   (3,4), 5, 2
                   (4,5), 6, 3
      """
      result = callZooFunc(
          "float", "unroll", data_rdd, unroll_length, predict_step
      )
      return cls._to_indexed_rdd(result)
  
imports: import warnings
  from zoo.pipeline.api.keras.models import Sequential
  from zoo.pipeline.api.keras.layers import *
  from zoo.models.common import *
  from zoo.common.utils import callZooFunc
  from bigdl.util.common import Sample
label_desc: Use image node_id 5 for calling the AnomalyDetector obj's underlying member method code with example usage: obj.unroll(cls, data_rdd, unroll_length, predict_step) and returns: cls