code: def _check(gt_labels, pred_labels):
      if gt_labels.ndim != 1:
          raise ValueError(
              "gt_labels must be 1D: shape is %r" % (gt_labels.shape,)
          )
      if pred_labels.ndim != 1:
          raise ValueError(
              "pred_labels must be 1D: shape is %r"
              % (pred_labels.shape,)
          )
      if gt_labels.shape != pred_labels.shape:
          raise ValueError(
              "gt_labels and pred_labels must have same size, got %d and %d"
              % (gt_labels.shape[0], pred_labels.shape[0])
          )
      return gt_labels, pred_labels
  
file_path: dgl/examples/pytorch/hilander/utils/metrics.py
imports: from __future__ import division
  import numpy
  from sklearn.metrics import precision_score, recall_score
  from sklearn.metrics.cluster import contingency_matrix, normalized_mutual_info_score
label_desc: Use image node_id 1 for calling a global function with example usage: _check(gt_labels, pred_labels) and returns: gt_labels,  pred_labels