code: def predict(neigh_ind, _y, n_neighbors):
      import scipy.stats as stats
  
      neigh_ind = neigh_ind.astype(np.int32)
      if isinstance(_y, cp.ndarray):
          _y = _y.get()
      if isinstance(neigh_ind, cp.ndarray):
          neigh_ind = neigh_ind.get()
  
      ypred, count = stats.mode(_y[neigh_ind], axis=1)
      return ypred.ravel(), count.ravel() * 1.0 / n_neighbors
  
imports: import gc
  from cuml.common import has_scipy
  import cuml
  import sklearn
  from cuml.internals.safe_imports import cpu_only_import_from
  from numpy.testing import assert_array_equal, assert_allclose
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  import math
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.neighbors import NearestNeighbors
  from sklearn.neighbors import NearestNeighbors
  from cuml.datasets import make_blobs
  from sklearn.metrics import pairwise_distances
  from cuml.metrics import pairwise_distances
  from cuml.internals import logger
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: predict(neigh_ind, _y, n_neighbors) and returns: ypred,  unknown