class_docstr: The class containing all get methods for dlm class.
  
  Methods:
      _getComponent: get the component if it is in dlm
      _getLatentState: get the latent state for a given component
      _getLatentCov: get the latent covariance for a given component
      _getComponentMean: get the mean of a given component
      _getComponentVar: get the variance of a given component
docstr: Get the latent states of a given component.
  
  Args:
      name: the name of the component.
      filterType: the type of the latent states to be returned.
                  could be "forwardFilter", "backwardSmoother" or
                  "predict".
      start: the start date for the latent states to be returned.
      end: the end date to be returned.
  
  Returns:
      A list of latent states.
docstr_tok: ["Get","the","latent","states","of","a","given","component",".","Args",":","name",":","the","name","of","the","component",".","filterType",":","the","type","of","the","latent","states","to","be","returned",".","could","be","``","forwardFilter","''",",","``","backwardSmoother","''","or","``","predict","''",".","start",":","the","start","date","for","the","latent","states","to","be","returned",".","end",":","the","end","date","to","be","returned",".","Returns",":","A","list","of","latent","states","."]
code: def _getLatentState(self, name, filterType, start, end):
      """Get the latent states of a given component.
  
      Args:
          name: the name of the component.
          filterType: the type of the latent states to be returned.
                      could be "forwardFilter", "backwardSmoother" or
                      "predict".
          start: the start date for the latent states to be returned.
          end: the end date to be returned.
  
      Returns:
          A list of latent states.
      """
      end += 1
      indx = self.builder.componentIndex[name]
      patten = (
          lambda x: x if x is None else x[indx[0] : (indx[1] + 1), 0]
      )
  
      if filterType == "forwardFilter":
          return list(map(patten, self.result.filteredState[start:end]))
      elif filterType == "backwardSmoother":
          return list(map(patten, self.result.smoothedState[start:end]))
      elif filterType == "predict":
          return list(
              map(patten, self.result.predictedState[start:end])
          )
      else:
          raise NameError("Incorrect filter type")
  
imports: from numpy import dot
  from pydlm.core._dlm import _dlm
label_desc: Use image node_id 1 for calling the _dlmGet obj's underlying member method code with example usage: obj._getLatentState(name, filterType, start, end) and returns: list, list, list