parameters: (self, transform_func=None)
return_statement: return EgoGraph(vertex_tensor, neighbors, None, self.nbr_nums)
docstring: transforms `EgoGraph`. Default transformation is encoding nodes feature 
      to embedding.
      Args:
        transform_func: A function that takes in an `EgoGraph` object and returns 
          a transformed version.
docstring_summary: transforms `EgoGraph`. Default transformation is encoding nodes feature 
      to embedding.
      Args:
        transform_func: A function that takes in an `EgoGraph` object and returns 
          a transformed version.
docstring_tokens: transforms EgoGraph . Default transformation is encoding nodes feature to embedding . Args : transform_func : A function that takes in an EgoGraph object and returns a transformed version .
function: def transform(self, transform_func=None):
      """transforms `EgoGraph`. Default transformation is encoding nodes feature 
      to embedding.
      Args:
        transform_func: A function that takes in an `EgoGraph` object and returns 
          a transformed version. 
      """
      if self.node_schema is None:
        return self
  
      assert len(self.node_schema) == (len(self.nbr_nodes) + 1)
  
      s = self.node_schema[0]
      vertex_handler = FeatureHandler(s[0], s[1])
      vertex_tensor = vertex_handler.forward(self.src)
  
      neighbors = []
      for i, nbr in enumerate(self.nbr_nodes):
        s = self.node_schema[i + 1]
        neighbor_handler = FeatureHandler(s[0], s[1])
        neighbor_tensor = neighbor_handler.forward(self.nbr_nodes[i])
        neighbors.append(neighbor_tensor)
  
      return EgoGraph(vertex_tensor, neighbors, None, self.nbr_nums)
function_tokens: def transform ( self , transform_func = None ) : if self . node_schema is None : return self assert len ( self . node_schema ) == ( len ( self . nbr_nodes ) + 1 ) s = self . node_schema [ 0 ] vertex_handler = FeatureHandler ( s [ 0 ] , s [ 1 ] ) vertex_tensor = vertex_handler . forward ( self . src ) neighbors = [ ] for i , nbr in enumerate ( self . nbr_nodes ) : s = self . node_schema [ i + 1 ] neighbor_handler = FeatureHandler ( s [ 0 ] , s [ 1 ] ) neighbor_tensor = neighbor_handler . forward ( self . nbr_nodes [ i ] ) neighbors . append ( neighbor_tensor ) return EgoGraph ( vertex_tensor , neighbors , None , self . nbr_nums )