code: def test_feature_storage_from_numpy():
      ar1 = np.random.randint(low=0, high=100, size=100_000)
      ar2 = np.random.randint(low=0, high=100, size=100_000)
      ar3 = np.random.randint(low=0, high=100, size=100_000).reshape(
          10_000, -1
      )
      fs = FeatureStore(backend="numpy")
      fs.add_data(ar1, "type1", "feat1")
      fs.add_data(ar2, "type1", "feat2")
      fs.add_data(ar3, "type2", "feat1")
  
      indices_to_fetch = np.random.randint(
          low=0, high=len(ar1), size=1024
      )
      output_fs = fs.get_data(
          indices_to_fetch, type_name="type1", feat_name="feat1"
      )
      expected = ar1[indices_to_fetch]
      np.testing.assert_array_equal(output_fs, expected)
  
      indices_to_fetch = np.random.randint(
          low=0, high=len(ar2), size=1024
      )
      output_fs = fs.get_data(
          indices_to_fetch, type_name="type1", feat_name="feat2"
      )
      expected = ar2[indices_to_fetch]
      np.testing.assert_array_equal(output_fs, expected)
  
      indices_to_fetch = np.random.randint(
          low=0, high=len(ar3), size=1024
      )
      output_fs = fs.get_data(
          indices_to_fetch, type_name="type2", feat_name="feat1"
      )
      expected = ar3[indices_to_fetch]
      np.testing.assert_array_equal(output_fs, expected)
  
imports: import pytest
  import numpy
  import cudf
  from cugraph.gnn import FeatureStore
label_desc: Use image node_id 1 for calling a global function with example usage: test_feature_storage_from_numpy() without return types