code: def reorder_missing_vector(
      vector, missing, inplace=False, prefix=None
  ):
      """
      Reorder the elements of a time-varying vector where all non-missing
      values are in the first elements of the vector.
  
      Parameters
      ----------
      vector : array_like
          The vector to be reordered. Must have shape (n, nobs).
      missing : array_like of bool
          The vector of missing indices. Must have shape (n, nobs).
      inplace : bool, optional
          Whether or not to reorder the matrix in-place. Default is False.
      prefix : {'s', 'd', 'c', 'z'}, optional
          The Fortran prefix of the vector. Default is to automatically detect
          the dtype. This parameter should only be used with caution.
  
      Returns
      -------
      reordered_vector : array_like
          The reordered vector.
      """
      if prefix is None:
          prefix = find_best_blas_type((vector,))[0]
      reorder = prefix_reorder_missing_vector_map[prefix]
  
      if not inplace:
          vector = np.copy(vector, order="F")
  
      reorder(vector, np.asfortranarray(missing))
  
      return vector
  
imports: import numpy
  from scipy.linalg import solve_sylvester
  import pandas
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.data import _is_using_pandas
  from scipy.linalg.blas import find_best_blas_type
  from .None import _initialization, _representation, _kalman_filter, _kalman_smoother, _simulation_smoother, _cfa_simulation_smoother, _tools
label_desc: Use image node_id 23 for calling a global function with example usage: reorder_missing_vector(vector, missing, inplace, prefix) and returns: vector