class_docstr: Class for a general Kalman filter working on Lie groups.
  
  Given an adapted model, it provides the tools to carry out non-linear state
  estimation with an error modeled on the Lie algebra. The model must provide
  the functions to propagate and update a state, the observation model, and
  the computation of the Jacobians.
  
  Parameter
  ---------
  model : {class, instance}
      Object representing an observed dynamical system.
docstr: Set the values of the covariances.
docstr_tok: ["Set","the","values","of","the","covariances","."]
code: def initialize_covariances(
      self, prior_values, process_values, obs_values
  ):
      """Set the values of the covariances."""
      cov_dict = {
          "covariance": prior_values,
          "process_noise": process_values,
          "measurement_noise": obs_values,
      }
      for attribute, value in cov_dict.items():
          setattr(self, attribute, value)
  
imports: import geomstats.backend
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.matrices import Matrices
  from geomstats.geometry.special_euclidean import SpecialEuclidean
label_desc: Use image node_id 2 for calling the KalmanFilter obj's underlying member method code with example usage: obj.initialize_covariances(prior_values, process_values, obs_values) without return types