code: def reorder_missing_matrix(
      matrix,
      missing,
      reorder_rows=False,
      reorder_cols=False,
      is_diagonal=False,
      inplace=False,
      prefix=None,
  ):
      """
      Reorder the rows or columns of a time-varying matrix where all non-missing
      values are in the upper left corner of the matrix.
  
      Parameters
      ----------
      matrix : array_like
          The matrix to be reordered. Must have shape (n, m, nobs).
      missing : array_like of bool
          The vector of missing indices. Must have shape (k, nobs) where `k = n`
          if `reorder_rows is True` and `k = m` if `reorder_cols is True`.
      reorder_rows : bool, optional
          Whether or not the rows of the matrix should be re-ordered. Default
          is False.
      reorder_cols : bool, optional
          Whether or not the columns of the matrix should be re-ordered. Default
          is False.
      is_diagonal : bool, optional
          Whether or not the matrix is diagonal. If this is True, must also have
          `n = m`. Default is False.
      inplace : bool, optional
          Whether or not to reorder the matrix in-place.
      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_matrix : array_like
          The reordered matrix.
      """
      if prefix is None:
          prefix = find_best_blas_type((matrix,))[0]
      reorder = prefix_reorder_missing_matrix_map[prefix]
  
      if not inplace:
          matrix = np.copy(matrix, order="F")
  
      reorder(
          matrix,
          np.asfortranarray(missing),
          reorder_rows,
          reorder_cols,
          is_diagonal,
      )
  
      return matrix
  
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 22 for calling a global function with example usage: reorder_missing_matrix(matrix, missing, reorder_rows, reorder_cols, is_diagonal, inplace, prefix) and returns: matrix