code: def kfold_xval(model, data, folds, *model_args, **model_kwargs):
      """Perform k-fold cross-validation.
  
      It generate out-of-sample predictions for each measurement.
  
      Parameters
      ----------
      model : Model class instance
          The type of the model to use for prediction. The corresponding Fit
          object must have a `predict` function implemented One of the following:
          `reconst.dti.TensorModel` or
          `reconst.csdeconv.ConstrainedSphericalDeconvModel`.
      data : ndarray
          Diffusion MRI data acquired with the GradientTable of the model. Shape
          will typically be `(x, y, z, b)` where `xyz` are spatial dimensions and
          b is the number of bvals/bvecs in the GradientTable.
      folds : int
          The number of divisions to apply to the data
      model_args : list
          Additional arguments to the model initialization
      model_kwargs : dict
          Additional key-word arguments to the model initialization. If contains
          the kwarg `mask`, this will be used as a key-word argument to the `fit`
          method of the model object, rather than being used in the
          initialization of the model object
  
      Notes
      -----
      This function assumes that a prediction API is implemented in the Model
      class for which prediction is conducted. That is, the Fit object that gets
      generated upon fitting the model needs to have a `predict` method, which
      receives a GradientTable class instance as input and produces a predicted
      signal as output.
  
      It also assumes that the model object has `bval` and `bvec` attributes
      holding b-values and corresponding unit vectors.
  
      References
      ----------
      .. [1] Rokem, A., Chan, K.L. Yeatman, J.D., Pestilli, F., Mezer, A.,
         Wandell, B.A., 2014. Evaluating the accuracy of diffusion models at
         multiple b-values with cross-validation. ISMRM 2014.
  
      """
      rng = model_kwargs.pop("rng", np.random.default_rng())
  
      # This should always be there, if the model inherits from
      # dipy.reconst.base.ReconstModel:
      gtab = model.gtab
      data_b = data[..., ~gtab.b0s_mask]
      div_by_folds = np.mod(data_b.shape[-1], folds)
      # Make sure that an equal number of samples get left out in each fold:
      if div_by_folds != 0:
          msg = (
              "The number of folds must divide the diffusion-weighted "
          )
          msg += "data equally, but "
          msg = "np.mod(%s, %s) is %s" % (
              data_b.shape[-1],
              folds,
              div_by_folds,
          )
          raise ValueError(msg)
  
      data_0 = data[..., gtab.b0s_mask]
      S0 = np.mean(data_0, -1)
      n_in_fold = data_b.shape[-1] / folds
      prediction = np.zeros(data.shape)
      # We are going to leave out some randomly chosen samples in each iteration:
      order = np.random.permutation(data_b.shape[-1])
  
      nz_bval = gtab.bvals[~gtab.b0s_mask]
      nz_bvec = gtab.bvecs[~gtab.b0s_mask]
  
      # Pop the mask, if there is one, out here for use in every fold:
      mask = model_kwargs.pop("mask", None)
      gtgt = gt.gradient_table  # Shorthand
      for k in range(folds):
          fold_mask = np.ones(data_b.shape[-1], dtype=bool)
          fold_idx = order[
              int(k * n_in_fold) : int((k + 1) * n_in_fold)
          ]
          fold_mask[fold_idx] = False
          this_data = np.concatenate(
              [data_0, data_b[..., fold_mask]], -1
          )
  
          this_gtab = gtgt(
              np.hstack(
                  [gtab.bvals[gtab.b0s_mask], nz_bval[fold_mask]]
              ),
              np.concatenate(
                  [gtab.bvecs[gtab.b0s_mask], nz_bvec[fold_mask]]
              ),
          )
          left_out_gtab = gtgt(
              np.hstack(
                  [gtab.bvals[gtab.b0s_mask], nz_bval[~fold_mask]]
              ),
              np.concatenate(
                  [gtab.bvecs[gtab.b0s_mask], nz_bvec[~fold_mask]]
              ),
          )
          this_model = model.__class__(
              this_gtab, *model_args, **model_kwargs
          )
          this_fit = this_model.fit(this_data, mask=mask)
          if not hasattr(this_fit, "predict"):
              err_str = "Models of type: %s " % this_model.__class__
              err_str += (
                  "do not have an implementation of model prediction"
              )
              err_str += " and do not support cross-validation"
              raise ValueError(err_str)
          this_predict = S0[..., None] * this_fit.predict(
              left_out_gtab, S0=1
          )
  
          idx_to_assign = np.where(~gtab.b0s_mask)[0][~fold_mask]
          prediction[..., idx_to_assign] = this_predict[
              ..., np.sum(gtab.b0s_mask) :
          ]
  
      # For the b0 measurements
      prediction[..., gtab.b0s_mask] = S0[..., None]
      return prediction
  
label_desc: Use image node_id 2 for calling a global function with example usage: kfold_xval(model, data, folds) and returns: prediction