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: Precict on RDD[Sample].
docstr_tok: ["Precict","on","RDD","[","Sample","]","."]
code: def predict(self, x, batch_per_thread=8):
      """
      Precict on RDD[Sample].
      """
      results = callZooFunc(
          self.bigdl_type,
          "modelPredictRDD",
          self.value,
          x,
          batch_per_thread,
      )
      return results.map(lambda data: data.to_ndarray())
  
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 4 for calling the AnomalyDetector obj's underlying member method code with example usage: obj.predict(x, batch_per_thread) and returns: results