code: def in1d(arr, test_set):
      test_set = set(test_set)
      result = np.empty(arr.shape[0], dtype=np.bool_)
      for i in numba.prange(arr.shape[0]):
          if arr[i] in test_set:
              result[i] = True
          else:
              result[i] = False
  
      return result
  
imports: import numpy
  import numba
  from sklearn.base import BaseEstimator
  from sklearn.utils import check_array
  from umap.sparse import arr_intersect
  from umap.sparse import arr_union
  from umap.umap_ import UMAP, make_epochs_per_sample
  from umap.spectral import spectral_layout
  from umap.layouts import optimize_layout_aligned_euclidean
label_desc: Use image node_id 1 for calling a global function with example usage: in1d(arr, test_set) and returns: result