code: def _outlier_to_sample_mask(outliers):
      """Generate sample mask from outlier regressors.
  
      Parameters
      ----------
      outliers : pandas.DataFrame
          DataFrame of outlier regressors. The shape should be
          (number of volumes, number of outlier regressors).
  
      Returns
      -------
      sample_mask : numpy.ndarray
          Index array of shape indicating the volumes that are not
          outliers. (number of volumes - number of outlier regressors, ).
      """
      outliers_one_hot = outliers.copy()
      if outliers_one_hot.size == 0:  # Do not supply sample mask
          return None  # consistency with nilearn sample_mask
      outliers_one_hot = outliers_one_hot.sum(axis=1).values
      return np.where(outliers_one_hot == 0)[0]
  
imports: import numpy
  import pandas
label_desc: Use image node_id 4 for calling a global function with example usage: _outlier_to_sample_mask(outliers) and returns: np, None