class_docstr: Class for modeling a non-linear 2D localization problem.
  
  The state is composed of a planar orientation and position, and is thus a
  member of SE(2).
  A sensor provides the linear and angular speed, while another one provides
  sparse position observations.
docstr: Compute the matrix associated to the propagation noise.
  
  The noise being considered multiplicative, it is simply the identity
  scaled by the time step.
  
  Parameters
  ----------
  state : unused
  sensor_input : array-like, shape=[4]
      Vector representing the information from the sensor.
  
  Returns
  -------
  jacobian : array-like, shape=[dim_noise, dim]
      Jacobian of the propagation w.r.t. the noise.
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code: def noise_jacobian(self, state, sensor_input):
      r"""Compute the matrix associated to the propagation noise.
  
      The noise being considered multiplicative, it is simply the identity
      scaled by the time step.
  
      Parameters
      ----------
      state : unused
      sensor_input : array-like, shape=[4]
          Vector representing the information from the sensor.
  
      Returns
      -------
      jacobian : array-like, shape=[dim_noise, dim]
          Jacobian of the propagation w.r.t. the noise.
      """
      dt, _, _ = self.preprocess_input(sensor_input)
      return gs.sqrt(dt) * gs.eye(self.dim_noise)
  
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 8 for calling the Localization obj's underlying member method code with example usage: obj.noise_jacobian(state, sensor_input) and returns: unknown