code: def chunked_parallel_special_metric(
      X, Y=None, metric=hellinger, chunk_size=16
  ):
      if Y is None:
          XX, symmetrical = X, True
          row_size = col_size = X.shape[0]
      else:
          XX, symmetrical = Y, False
          row_size, col_size = X.shape[0], Y.shape[0]
  
      result = np.zeros((row_size, col_size), dtype=np.float32)
      n_row_chunks = (row_size // chunk_size) + 1
      for chunk_idx in numba.prange(n_row_chunks):
          n = chunk_idx * chunk_size
          chunk_end_n = min(n + chunk_size, row_size)
          m_start = n if symmetrical else 0
          for m in range(m_start, col_size, chunk_size):
              chunk_end_m = min(m + chunk_size, col_size)
              for i in range(n, chunk_end_n):
                  for j in range(m, chunk_end_m):
                      result[i, j] = metric(X[i], XX[j])
      return result
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 58 for calling a global function with example usage: chunked_parallel_special_metric(X, Y, metric, chunk_size) and returns: result