code: def compute_kernel_slow(Y, X, kernel, h):
      d = np.sqrt(((Y[:, None, :] - X) ** 2).sum(-1))
      norm = kernel_norm(h, X.shape[1], kernel)
  
      if kernel == "gaussian":
          return norm * np.exp(-0.5 * (d * d) / (h * h)).sum(-1)
      elif kernel == "tophat":
          return norm * (d < h).sum(-1)
      elif kernel == "epanechnikov":
          return norm * ((1.0 - (d * d) / (h * h)) * (d < h)).sum(-1)
      elif kernel == "exponential":
          return norm * (np.exp(-d / h)).sum(-1)
      elif kernel == "linear":
          return norm * ((1 - d / h) * (d < h)).sum(-1)
      elif kernel == "cosine":
          return norm * (np.cos(0.5 * np.pi * d / h) * (d < h)).sum(-1)
      else:
          raise ValueError("kernel not recognized")
  
imports: import itertools
  import pickle
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal
  from sklearn.metrics import DistanceMetric
  from sklearn.neighbors._ball_tree import BallTree, kernel_norm
  from sklearn.neighbors._ball_tree import NeighborsHeap64
  from sklearn.neighbors._ball_tree import nodeheap_sort
  from sklearn.neighbors._ball_tree import simultaneous_sort
  from sklearn.neighbors._kd_tree import KDTree
  from sklearn.neighbors._kd_tree import NeighborsHeap64
  from sklearn.neighbors._kd_tree import nodeheap_sort
  from sklearn.neighbors._kd_tree import simultaneous_sort
  from sklearn.utils import check_random_state
label_desc: Use image node_id 2 for calling a global function with example usage: compute_kernel_slow(Y, X, kernel, h) and returns: unknown, unknown, unknown, unknown, unknown, unknown