code: def predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.0):
      """
      Predict next state (prior) using the Kalman filter state propagation
      equations.
  
      Parameters
      ----------
  
      x : numpy.array
          State estimate vector
  
      P : numpy.array
          Covariance matrix
  
      F : numpy.array()
          State Transition matrix
  
      Q : numpy.array, Optional
          Process noise 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.
  
      alpha : float, Optional, default=1.0
          Fading memory setting. 1.0 gives the normal Kalman filter, and
          values slightly larger than 1.0 (such as 1.02) give a fading
          memory effect - previous measurements have less influence on the
          filter's estimates. This formulation of the Fading memory filter
          (there are many) is due to Dan Simon
  
      Returns
      -------
  
      x : numpy.array
          Prior state estimate vector
  
      P : numpy.array
          Prior covariance matrix
      """
  
      if np.isscalar(F):
          F = np.array(F)
      x = dot(F, x) + dot(B, u)
      P = (alpha * alpha) * dot(dot(F, P), F.T) + Q
  
      return x, P
  
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 3 for calling a global function with example usage: predict(x, P, F, Q, u, B, alpha) and returns: x,  P