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.
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docstr: # Arguments
  :param ytruth: RDD of float or double values. Truth to be compared.
  :param ypredict: RDD of float or double values. Predictions.
  :param anomaly_size: Int. The size to be considered as anomalies.
  :return: RDD of [ytruth, ypredict, anomaly], anomaly is None or ytruth
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code: def detect_anomalies(cls, ytruth, ypredict, anomaly_size):
      """
      # Arguments
      :param ytruth: RDD of float or double values. Truth to be compared.
      :param ypredict: RDD of float or double values. Predictions.
      :param anomaly_size: Int. The size to be considered as anomalies.
      :return: RDD of [ytruth, ypredict, anomaly], anomaly is None or ytruth
      """
      return callZooFunc(
          "float", "detectAnomalies", ytruth, ypredict, anomaly_size
      )
  
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 6 for calling the AnomalyDetector obj's underlying member method code with example usage: obj.detect_anomalies(cls, ytruth, ypredict, anomaly_size) and returns: callZooFunc