code: def trustworthiness_vector_bulk(
      indices_source, indices_embedded, max_k
  ):  # pragma: no cover
      n_samples = indices_embedded.shape[0]
      trustworthiness = np.zeros(max_k + 1, dtype=np.float64)
  
      for i in range(n_samples):
          for j in range(max_k):
              rank = 0
              while indices_source[i, rank] != indices_embedded[i, j]:
                  rank += 1
  
              for k in range(j + 1, max_k + 1):
                  if rank > k:
                      trustworthiness[k] += rank - k
  
      for k in range(1, max_k + 1):
          trustworthiness[k] = 1.0 - trustworthiness[k] * (
              2.0 / (n_samples * k * (2.0 * n_samples - 3.0 * k - 1.0))
          )
  
      return trustworthiness
  
imports: import numpy
  import numba
  from sklearn.neighbors import KDTree
  from umap.distances import named_distances
label_desc: Use image node_id 1 for calling a global function with example usage: trustworthiness_vector_bulk(indices_source, indices_embedded, max_k) and returns: trustworthiness