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 __init__(
      self,
      feature_shape,
      hidden_layers=[8, 32, 15],
      dropouts=[0.2, 0.2, 0.2],
      **kwargs,
  ):
      assert len(hidden_layers) == len(
          dropouts
      ), "sizes of dropouts and hidden_layers should be equal"
      if "bigdl_type" in kwargs:
          kwargs.pop("bigdl_type")
          self.bigdl_type = kwargs.get("bigdl_type")
      else:
          self.bigdl_type = "float"
      if kwargs:
          raise TypeError(
              "Wrong arguments for AnomalyDetector: " + str(kwargs)
          )
      self.feature_shape = feature_shape
      self.hidden_layers = hidden_layers
      self.dropouts = dropouts
      self.model = self.build_model()
      super(AnomalyDetector, self).__init__(
          None,
          self.bigdl_type,
          feature_shape,
          hidden_layers,
          dropouts,
          self.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 1 to create a new AnomalyDetector object from inherited base classes: KerasZooModel with example: obj = AnomalyDetector(feature_shape, hidden_layers, dropouts)