class_docstr: This module implement base class for graph wrapper.
  
  Currently our PGL is developed based on static computational mode of
  paddle (we'll support dynamic computational model later). We need to build
  model upon a virtual data holder. BaseGraphWrapper provide a virtual
  graph structure that users can build deep learning models
  based on this virtual graph. And then feed real graph data to run
  the models. Moreover, we provide convenient message-passing interface
  (send & recv) for building graph neural networks.
  
  NOTICE: Don't use this BaseGraphWrapper directly. Use :code:`GraphWrapper`
  and :code:`StaticGraphWrapper` to create graph wrapper instead.
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docstr: Return a dictionary of tensor representing edge features.
  
  Return:
      A dictionary whose keys are the feature names and the values
      are feature tensor.
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code: def edge_feat(self):
      """Return a dictionary of tensor representing edge features.
  
      Return:
          A dictionary whose keys are the feature names and the values
          are feature tensor.
      """
      return self.edge_feat_tensor_dict
  
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 9 for calling the BaseGraphWrapper obj's underlying member method code with example usage: obj.edge_feat() and returns: self