code: def to_matrix_vector(transform):
      """Split an homogeneous transform into its matrix and vector components.
  
      The transformation must be represented in homogeneous coordinates.
      It is split into its linear transformation matrix and translation vector
      components.
  
      This function does not normalize the matrix. This means that for it to be
      the inverse of from_matrix_vector, transform[-1, -1] must equal 1, and
      transform[-1, :-1] must equal 0.
  
      Parameters
      ----------
      transform : numpy.ndarray
          Homogeneous transform matrix. Example: a (4, 4) transform representing
          linear transformation and translation in 3 dimensions.
  
      Returns
      -------
      matrix, vector : numpy.ndarray
          The matrix and vector components of the transform matrix.  For
          an (N, N) transform, matrix will be (N-1, N-1) and vector will be
          a 1D array of shape (N-1,).
  
      See Also
      --------
      from_matrix_vector
  
      """
      ndimin = transform.shape[0] - 1
      ndimout = transform.shape[1] - 1
      matrix = transform[0:ndimin, 0:ndimout]
      vector = transform[0:ndimin, ndimout]
      return matrix, vector
  
file_path: nilearn/nilearn/image/resampling.py
imports: import numbers
  import warnings
  import numpy
  from scipy import linalg
  from scipy.ndimage import affine_transform, find_objects
  from ..None import _utils
  from .._utils import stringify_path
  from .._utils.niimg import _get_data
  from .image import copy_img, crop_img
label_desc: Use image node_id 1 for calling a global function with example usage: to_matrix_vector(transform) and returns: matrix,  vector