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: Copy result from the model to _result class
  
          
docstr_tok: ["Copy","result","from","the","model","to","_result","class"]
code: def _copy(self, model, result, step, filterType):
      """Copy result from the model to _result class"""
  
      if filterType == "forwardFilter":
          result.filteredObs[step] = model.obs
          result.predictedObs[step] = model.prediction.obs
          result.filteredObsVar[step] = model.obsVar
          result.predictedObsVar[step] = model.prediction.obsVar
          result.filteredState[step] = model.state
          result.predictedState[step] = model.prediction.state
          result.filteredCov[step] = model.sysVar
          result.predictedCov[step] = model.prediction.sysVar
          result.noiseVar[step] = model.noiseVar
          result.df[step] = model.df
          # pad missing value with filtered result
          if self.data[step] is None:
              self.padded_data[step] = result.filteredObs[step]
  
      elif filterType == "backwardSmoother":
          result.smoothedState[step] = model.state
          result.smoothedObs[step] = model.obs
          result.smoothedCov[step] = model.sysVar
          result.smoothedObsVar[step] = model.obsVar
  
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 10 for calling the _dlm obj's underlying member method code with example usage: obj._copy(model, result, step, filterType) without return types