code: def triangle_count(G, start_list=None):
      """
      Compute the number of triangles (cycles of length three) in the
      input graph.
  
      Parameters
      ----------
      G : cugraph.graph or networkx.Graph
          cuGraph graph descriptor, should contain the connectivity information,
          (edge weights are not used in this algorithm).
          The current implementation only supports undirected graphs.
  
      start_list : list or cudf.Series
          list of vertices for triangle count. if None the entire set of vertices
          in the graph is processed
  
      Returns
      -------
      result : cudf.DataFrame
          GPU data frame containing 2 cudf.Series
  
      ddf['vertex']: cudf.Series
              Contains the triangle counting vertices
      ddf['counts']: cudf.Series
          Contains the triangle counting counts
  
      Examples
      --------
      >>> gdf = cudf.read_csv(datasets_path / 'karate.csv',
      ...                     delimiter = ' ',
      ...                     dtype=['int32', 'int32', 'float32'],
      ...                     header=None)
      >>> G = cugraph.Graph()
      >>> G.from_cudf_edgelist(gdf, source='0', destination='1', edge_attr='2')
      >>> count = cugraph.triangle_count(G)
  
      """
      G, _ = ensure_cugraph_obj_for_nx(G)
  
      if G.is_directed():
          raise ValueError("input graph must be undirected")
  
      if start_list is not None:
          if isinstance(start_list, int):
              start_list = [start_list]
          if isinstance(start_list, list):
              start_list = cudf.Series(start_list)
  
          if not isinstance(start_list, cudf.Series):
              raise TypeError(
                  f"'start_list' must be either a list or a cudf.Series,"
                  f"got: {start_list.dtype}"
              )
          start_list = ensure_valid_dtype(G, start_list)
  
          if G.renumbered is True:
              if isinstance(start_list, cudf.DataFrame):
                  start_list = G.lookup_internal_vertex_id(
                      start_list, start_list.columns
                  )
              else:
                  start_list = G.lookup_internal_vertex_id(start_list)
  
      do_expensive_check = False
  
      vertex, counts = pylibcugraph_triangle_count(
          resource_handle=ResourceHandle(),
          graph=G._plc_graph,
          start_list=start_list,
          do_expensive_check=do_expensive_check,
      )
  
      df = cudf.DataFrame()
      df["vertex"] = vertex
      df["counts"] = counts
  
      if G.renumbered:
          df = G.unrenumber(df, "vertex")
  
      return df
  
imports: from cugraph.utilities import ensure_cugraph_obj_for_nx
  import cudf
  from pylibcugraph import triangle_count
  from pylibcugraph import ResourceHandle
  import warnings
label_desc: Use image node_id 2 for calling a global function with example usage: triangle_count(G, start_list) and returns: df