class_docstr: _dlm includes all basic functions for building dlm model.
  
  Attributes:
      data: the observed time series data
      n: the length of the time series data
      result: the inner class that records the filtered and smoothed results
      builder: the @builder that is used for providing the modeling
      functionality. For details please refer to @builder
      Filter: the filter used for filtering time series data using the model.
              For details please refer to @kalmanFilter
      initialized: indicates whether the dynamic linear model has been
                   initialized
      options: model options, including initial guess of the observational
               variance.
               More is going to be added (plot options and shrinkage options)
      time: the time label, used for plotting
  
  
  Methods:
      _initialize: initialize the dlm (builder and kalmanFilter)
      _forwardFilter: run forward filter for a specific start and end date
      _backwardSmoother: run backward smooth for a specific start and end
                         date
      _predictInSample: predict the latent state and observation for a given
                        period of time (deprecated)
      _resetModelStatus: reset the model status to its prior status
      _setModelStatus: set the model status to a specific date
      _defaultOptions: a class to store and set default options
      _result: a class to store the results
      _copy: copy the result from the model to the _result class
      _reverseCopy: copy the result from the _result class to the model
      _checkFeatureSize: check whether the features's n matches the data's n
      _checkComponent: check whether a component is in dlm
      _checkPlotOptions: set the correct options according to the fit
      _checkAndGetWorkingDates: get the correct filtering dates
docstr: Auto initialize the noise parameter if options.useAutoNoise
  is true.
docstr_tok: ["Auto","initialize","the","noise","parameter","if","options.useAutoNoise","is","true","."]
code: def _autoNoise(self):
      """Auto initialize the noise parameter if options.useAutoNoise
      is true.
  
      """
      if self.options.useAutoNoise:
          trimmed_data = [x for x in self.data if x is not None]
          self.options.noise = min(var(trimmed_data), 1)
  
imports: from pydlm.base.kalmanFilter import kalmanFilter
  from pydlm.modeler.builder import builder
  from numpy import var
  import logging
label_desc: Use image node_id 4 for calling the _dlm obj's underlying member method code with example usage: obj._autoNoise() without return types