code: def test_backend_numpy(
      client_with_property_csvs_loaded, pG_with_property_csvs_loaded
  ):
      rpG = RemoteGraph(client_with_property_csvs_loaded, 0)
      pG = pG_with_property_csvs_loaded
  
      # edges()
      rpg_edges = rpG.edges(backend="numpy")
      pg_edges = pG.get_edge_data(
          columns=[pG.src_col_name, pG.dst_col_name, pG.type_col_name]
      )
      for out_tensor in rpg_edges:
          assert isinstance(out_tensor, np.ndarray)
      assert (
          rpg_edges[1].tolist()
          == pg_edges[pG.src_col_name].values_host.tolist()
      )
      assert (
          rpg_edges[2].tolist()
          == pg_edges[pG.dst_col_name].values_host.tolist()
      )
      assert (
          rpg_edges[0].tolist()
          == pg_edges[pG.edge_id_col_name].values_host.tolist()
      )
  
      rpg_types = rpg_edges[3].tolist()
      pg_types = [
          rpG._edge_categorical_dtype[t]
          for t in pg_edges[pG.type_col_name].values_host
      ]
      assert rpg_types == pg_types
  
      # get_vertex_data()
      cols_of_interest = [
          "merchant_location",
          "merchant_size",
          "merchant_sales",
          "merchant_num_employees",
      ]
      rpg_vertex_data = rpG.get_vertex_data(
          types=["merchants"], columns=cols_of_interest, backend="numpy"
      )
      pg_vertex_data = pG.get_vertex_data(
          types=["merchants"], columns=cols_of_interest
      ).fillna(0)
      for out_tensor in rpg_vertex_data:
          assert isinstance(out_tensor, np.ndarray)
      assert len(rpg_vertex_data) == len(pg_vertex_data.columns)
      for i, col in enumerate(cols_of_interest):
          assert (
              rpg_vertex_data[i + 2].tolist()
              == pg_vertex_data[col].values_host.tolist()
          )
  
      # get_edge_data()
      cols_of_interest = ["time", "volume", "card_num"]
      rpg_edge_data = rpG.get_edge_data(
          types=["transactions"],
          columns=cols_of_interest,
          backend="numpy",
      )
      pg_edge_data = pG.get_edge_data(
          types=["transactions"], columns=cols_of_interest
      ).fillna(0)
      for out_tensor in rpg_edge_data:
          assert isinstance(out_tensor, np.ndarray)
      assert len(rpg_edge_data) == len(pg_edge_data.columns)
      for i, col in enumerate(cols_of_interest):
          assert (
              rpg_edge_data[i + 4].tolist()
              == pg_edge_data[col].values_host.tolist()
          )
  
imports: import importlib
  import random
  import pytest
  import pandas
  import numpy
  import cupy
  import cudf
  import cugraph
  from cugraph.experimental import PropertyGraph
  from cugraph_service_client import RemoteGraph
  from .None import data
label_desc: Use image node_id 16 for calling a global function with example usage: test_backend_numpy(client_with_property_csvs_loaded, pG_with_property_csvs_loaded) without return types