code: def reorient_bvecs(gtab, affines, atol=1e-2):
      """Reorient the directions in a GradientTable.
  
      When correcting for motion, rotation of the diffusion-weighted volumes
      might cause systematic bias in rotationally invariant measures, such as FA
      and MD, and also cause characteristic biases in tractography, unless the
      gradient directions are appropriately reoriented to compensate for this
      effect [Leemans2009]_.
  
      Parameters
      ----------
      gtab : GradientTable
          The nominal gradient table with which the data were acquired.
      affines : list or ndarray of shape (4, 4, n) or (3, 3, n)
          Each entry in this list or array contain either an affine
          transformation (4,4) or a rotation matrix (3, 3).
          In both cases, the transformations encode the rotation that was applied
          to the image corresponding to one of the non-zero gradient directions
          (ordered according to their order in `gtab.bvecs[~gtab.b0s_mask]`)
      atol: see gradient_table()
  
      Returns
      -------
      gtab : a GradientTable class instance with the reoriented directions
  
      References
      ----------
      .. [Leemans2009] The B-Matrix Must Be Rotated When Correcting for
         Subject Motion in DTI Data. Leemans, A. and Jones, D.K. (2009).
         MRM, 61: 1336-1349
      """
      if isinstance(affines, list):
          affines = np.stack(affines, axis=-1)
  
      if affines.shape[0] != affines.shape[1]:
          msg = """reorient_bvecs has changed to require affines as
          (4, 4, n) or (3, 3, n). Shape of (n, 4, 4) or (n, 3, 3)
          is deprecated and will fail in the future."""
          warn(msg, UserWarning)
          affines = np.moveaxis(affines, 0, -1)
  
      new_bvecs = gtab.bvecs[~gtab.b0s_mask]
  
      if new_bvecs.shape[0] != affines.shape[-1]:
          e_s = "Number of affine transformations must match number of "
          e_s += "non-zero gradients"
          raise ValueError(e_s)
  
      # moving axis to make life easier
      affines = np.moveaxis(affines, -1, 0)
  
      for i, aff in enumerate(affines):
          if aff.shape == (4, 4):
              # This must be an affine!
              # Remove the translation component:
              aff = aff[:3, :3]
          # Decompose into rotation and scaling components:
          R, S = polar(aff)
          Rinv = inv(R)
          # Apply the inverse of the rotation to the corresponding gradient
          # direction:
          new_bvecs[i] = np.dot(Rinv, new_bvecs[i])
  
      return_bvecs = np.zeros(gtab.bvecs.shape)
      return_bvecs[~gtab.b0s_mask] = new_bvecs
      return gradient_table(
          gtab.bvals,
          return_bvecs,
          big_delta=gtab.big_delta,
          small_delta=gtab.small_delta,
          b0_threshold=gtab.b0_threshold,
          atol=atol,
      )
  
imports: import logging
  from warnings import warn
  import numpy
  from scipy.linalg import inv, polar
  from dipy.io import gradients
  from dipy.core.onetime import auto_attr
  from dipy.core.geometry import vector_norm, vec2vec_rotmat
  from dipy.core.sphere import disperse_charges, HemiSphere
  from dipy.utils.deprecator import deprecate_with_version
label_desc: Use image node_id 9 for calling a global function with example usage: reorient_bvecs(gtab, affines, atol) and returns: gradient_table