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","."]
code: def _to_indexed_rdd(unrolled_rdd):
      def row_to_feature(feature_str):
          feature = [x.split("|") for x in feature_str.split(",")]
          matrix = []
          for i in range(0, len(feature)):
              line = []
              for j in range(0, len(feature[0])):
                  line.append(float(feature[i][j]))
          matrix.append(line)
          return matrix
  
      return unrolled_rdd.map(
          lambda y: FeatureLableIndex(
              row_to_feature(y[0]), float(y[1]), long(y[2])
          )
      )
  
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 9 for calling the AnomalyDetector obj's underlying member method code with example usage: obj._to_indexed_rdd(unrolled_rdd) and returns: unrolled_rdd, matrix