docstr: Returns the graph adjacency matrix as a NumPy array.
docstr_tok: ["Returns","the","graph","adjacency","matrix","as","a","NumPy","array","."]
code: def to_numpy_array(self):
      """
      Returns the graph adjacency matrix as a NumPy array.
      """
  
      nlen = self.number_of_nodes()
      elen = self.number_of_edges()
      df = self.edgelist.edgelist_df
      np_array = np.full((nlen, nlen), 0.0)
      nodes = self.nodes()
      for i in range(0, elen):
          # Map vertices to consecutive integers
          idx_src = nodes[
              nodes == df[simpleGraphImpl.srcCol].iloc[i]
          ].index[0]
          idx_dst = nodes[
              nodes == df[simpleGraphImpl.dstCol].iloc[i]
          ].index[0]
          np_array[idx_src, idx_dst] = df[self.edgeWeightCol].iloc[i]
      return np_array
  
imports: from cugraph.structure import graph_primtypes_wrapper
  from cugraph.structure.graph_primtypes_wrapper import Direction
  from cugraph.structure.symmetrize import symmetrize
  from cugraph.structure.number_map import NumberMap
  import cugraph.dask.common.mg_utils
  import cudf
  import dask_cudf
  import cugraph.dask.comms.comms
  import pandas
  import numpy
  import warnings
  from cugraph.dask.structure import replication
  from typing import Union, Dict
  from pylibcugraph import get_two_hop_neighbors, select_random_vertices
  from pylibcugraph import ResourceHandle, GraphProperties, SGGraph
label_desc: Use image node_id 5 for calling the simpleGraphImpl obj's underlying member method code with example usage: obj.to_numpy_array() and returns: np_array