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 build_model(self):
      model = Sequential()
      model.add(InputLayer(input_shape=self.feature_shape)).add(
          LSTM(
              input_shape=self.feature_shape,
              output_dim=self.hidden_layers[0],
              return_sequences=True,
          )
      )
  
      for ilayer in range(1, len(self.hidden_layers) - 1):
          model.add(
              LSTM(
                  output_dim=self.hidden_layers[ilayer],
                  return_sequences=True,
              )
          ).add(Dropout(self.dropouts[ilayer]))
  
      model.add(
          LSTM(self.hidden_layers[-1], return_sequences=False)
      ).add(Dropout(self.dropouts[-1]))
  
      model.add(Dense(output_dim=1))
      return model
  
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 2 for calling the AnomalyDetector obj's underlying member method code with example usage: obj.build_model() and returns: model