class_docstr: Class for modeling a linear 1D localization problem.
  
  The state is made of a scalar position and scalar speed, thus a 2D vector.
  A sensor provides acceleration inputs, while another one provides sparse
  measurements of the position.
docstr: Get the observation covariance.
  
  Parameters
  ----------
  state : unused
  observation_cov : array-like, shape=[dim_obs, dim_obs]
      Covariance matrix associated to the sensor.
  
  Returns
  -------
  covariance : array-like, shape=[dim_obs, dim_obs]
      Covariance of the observation.
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code: def get_measurement_noise_cov(state, observation_cov):
      r"""Get the observation covariance.
  
      Parameters
      ----------
      state : unused
      observation_cov : array-like, shape=[dim_obs, dim_obs]
          Covariance matrix associated to the sensor.
  
      Returns
      -------
      covariance : array-like, shape=[dim_obs, dim_obs]
          Covariance of the observation.
      """
      return gs.copy(observation_cov)
  
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 6 for calling the LocalizationLinear obj's underlying member method code with example usage: obj.get_measurement_noise_cov(state, observation_cov) and returns: gs