code: def predict_steadystate(x, F=1, u=0, B=1):
      """
      Predict next state (prior) using the Kalman filter state propagation
      equations. This steady state form only computes x, assuming that the
      covariance is constant.
  
      Parameters
      ----------
  
      x : numpy.array
          State estimate vector
  
      P : numpy.array
          Covariance matrix
  
      F : numpy.array()
          State Transition matrix
  
      u : numpy.array, Optional, default 0.
          Control vector. If non-zero, it is multiplied by B
          to create the control input into the system.
  
      B : numpy.array, optional, default 0.
          Control transition matrix.
  
      Returns
      -------
  
      x : numpy.array
          Prior state estimate vector
      """
  
      if np.isscalar(F):
          F = np.array(F)
      x = dot(F, x) + dot(B, u)
  
      return x
  
imports: from __future__ import absolute_import, division
  from copy import deepcopy
  from math import log, exp, sqrt
  import sys
  import numpy
  from numpy import dot, zeros, eye, isscalar, shape
  import numpy.linalg
  from filterpy.stats import logpdf
  from filterpy.common import pretty_str, reshape_z
label_desc: Use image node_id 4 for calling a global function with example usage: predict_steadystate(x, F, u, B) and returns: x