class_docstr: Implement a graph wrapper that creates a graph data holders
  that attributes and features in the graph are :code:`L.data`.
  And we provide interface :code:`to_feed` to help converting :code:`Graph`
  data into :code:`feed_dict`.
  
  Args:
      name: The graph data prefix
  
      node_feat: A list of tuples that decribe the details of node
                 feature tenosr. Each tuple mush be (name, shape, dtype)
                 and the first dimension of the shape must be set unknown
                 (-1 or None) or we can easily use :code:`Graph.node_feat_info()`
                 to get the node_feat settings.
  
      edge_feat: A list of tuples that decribe the details of edge
                 feature tenosr. Each tuple mush be (name, shape, dtype)
                 and the first dimension of the shape must be set unknown
                 (-1 or None) or we can easily use :code:`Graph.edge_feat_info()`
                 to get the edge_feat settings.
  
  Examples:
  
      .. code-block:: python
  
          import numpy as np
          import paddle.fluid as fluid
          from pgl.graph import Graph
          from pgl.graph_wrapper import GraphWrapper
  
          place = fluid.CPUPlace()
          exe = fluid.Excecutor(place)
  
          num_nodes = 5
          edges = [ (0, 1), (1, 2), (3, 4)]
          feature = np.random.randn(5, 100)
          edge_feature = np.random.randn(3, 100)
          graph = Graph(num_nodes=num_nodes,
                      edges=edges,
                      node_feat={
                          "feature": feature
                      },
                      edge_feat={
                          "edge_feature": edge_feature
                      })
  
          graph_wrapper = GraphWrapper(name="graph",
                      node_feat=graph.node_feat_info(),
                      edge_feat=graph.edge_feat_info())
  
          # build your deep graph model
          ...
  
          # Initialize parameters for deep graph model
          exe.run(fluid.default_startup_program())
  
          for i in range(10):
              feed_dict = graph_wrapper.to_feed(graph)
              ret = exe.run(fetch_list=[...], feed=feed_dict )
class_docstr_tok: ["Implement","a","graph","wrapper","that","creates","a","graph","data","holders","that","attributes","and","features","in","the","graph","are",":","code",":","`","L.data","`",".","And","we","provide","interface",":","code",":","`","to_feed","`","to","help","converting",":","code",":","`","Graph","`","data","into",":","code",":","`","feed_dict","`",".","Args",":","name",":","The","graph","data","prefix","node_feat",":","A","list","of","tuples","that","decribe","the","details","of","node","feature","tenosr",".","Each","tuple","mush","be","(","name",",","shape",",","dtype",")","and","the","first","dimension","of","the","shape","must","be","set","unknown","(","-1","or","None",")","or","we","can","easily","use",":","code",":","`","Graph.node_feat_info","(",")","`","to","get","the","node_feat","settings",".","edge_feat",":","A","list","of","tuples","that","decribe","the","details","of","edge","feature","tenosr",".","Each","tuple","mush","be","(","name",",","shape",",","dtype",")","and","the","first","dimension","of","the","shape","must","be","set","unknown","(","-1","or","None",")","or","we","can","easily","use",":","code",":","`","Graph.edge_feat_info","(",")","`","to","get","the","edge_feat","settings",".","Examples",":","..","code-block",":",":","python","import","numpy","as","np","import","paddle.fluid","as","fluid","from","pgl.graph","import","Graph","from","pgl.graph_wrapper","import","GraphWrapper","place","=","fluid.CPUPlace","(",")","exe","=","fluid.Excecutor","(","place",")","num_nodes","=","5","edges","=","[","(","0",",","1",")",",","(","1",",","2",")",",","(","3",",","4",")","]","feature","=","np.random.randn","(","5",",","100",")","edge_feature","=","np.random.randn","(","3",",","100",")","graph","=","Graph","(","num_nodes=num_nodes",",","edges=edges",",","node_feat=","{","``","feature","''",":","feature","}",",","edge_feat=","{","``","edge_feature","''",":","edge_feature","}",")","graph_wrapper","=","GraphWrapper","(","name=","''","graph","''",",","node_feat=graph.node_feat_info","(",")",",","edge_feat=graph.edge_feat_info","(",")",")","#","build","your","deep","graph","model","...","#","Initialize","parameters","for","deep","graph","model","exe.run","(","fluid.default_startup_program","(",")",")","for","i","in","range","(","10",")",":","feed_dict","=","graph_wrapper.to_feed","(","graph",")","ret","=","exe.run","(","fetch_list=","[","...","]",",","feed=feed_dict",")"]
docstr: Return the holder list.
          
docstr_tok: ["Return","the","holder","list","."]
code: def holder_list(self):
      """Return the holder list."""
      return self._holder_list
  
imports: import warnings
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.utils import op
  from pgl.utils import paddle_helper
  from pgl.utils.logger import log
label_desc: Use image node_id 6 for calling the GraphWrapper obj's underlying member method code with example usage: obj.holder_list() and returns: self