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: Load an existing AnomalyDetector model (with weights).
  
  # Arguments
  path: The path for the pre-defined model.
        Local file system, HDFS and Amazon S3 are supported.
        HDFS path should be like 'hdfs://[host]:[port]/xxx'.
        Amazon S3 path should be like 's3a://bucket/xxx'.
  weight_path: The path for pre-trained weights if any. Default is None.
docstr_tok: ["Load","an","existing","AnomalyDetector","model","(","with","weights",")",".","#","Arguments","path",":","The","path","for","the","pre-defined","model",".","Local","file","system",",","HDFS","and","Amazon","S3","are","supported",".","HDFS","path","should","be","like","'hdfs",":","\/\/","[","host","]",":","[","port","]","\/xxx","'",".","Amazon","S3","path","should","be","like","'s3a",":","\/\/bucket\/xxx","'",".","weight_path",":","The","path","for","pre-trained","weights","if","any",".","Default","is","None","."]
code: def load_model(path, weight_path=None, bigdl_type="float"):
      """
      Load an existing AnomalyDetector model (with weights).
  
      # Arguments
      path: The path for the pre-defined model.
            Local file system, HDFS and Amazon S3 are supported.
            HDFS path should be like 'hdfs://[host]:[port]/xxx'.
            Amazon S3 path should be like 's3a://bucket/xxx'.
      weight_path: The path for pre-trained weights if any. Default is None.
      """
      jmodel = callZooFunc(
          bigdl_type, "loadAnomalyDetector", path, weight_path
      )
      model = KerasZooModel._do_load(jmodel, bigdl_type)
      model.__class__ = AnomalyDetector
      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 3 for calling the AnomalyDetector obj's underlying member method code with example usage: obj.load_model(path, weight_path, bigdl_type) and returns: model