class_docstr: The class that can represent a batch of edges.
  
  Parameters
  ----------
  graph : DGLGraph
      Graph object.
  eid : Tensor
      Edge IDs.
  etype : (str, str, str)
      Edge type.
  src_data : dict[str, Tensor]
      Src node features.
  edge_data : dict[str, Tensor]
      Edge features.
  dst_data : dict[str, Tensor]
      Dst node features.
class_docstr_tok: ["The","class","that","can","represent","a","batch","of","edges",".","Parameters","--","--","--","--","--","graph",":","DGLGraph","Graph","object",".","eid",":","Tensor","Edge","IDs",".","etype",":","(","str",",","str",",","str",")","Edge","type",".","src_data",":","dict","[","str",",","Tensor","]","Src","node","features",".","edge_data",":","dict","[","str",",","Tensor","]","Edge","features",".","dst_data",":","dict","[","str",",","Tensor","]","Dst","node","features","."]
docstr: Return the canonical edge type (i.e. triplet of source, edge, and
  destination node type) for this edge batch.
docstr_tok: ["Return","the","canonical","edge","type","(","i.e",".","triplet","of","source",",","edge",",","and","destination","node","type",")","for","this","edge","batch","."]
code: def canonical_etype(self):
      """Return the canonical edge type (i.e. triplet of source, edge, and
      destination node type) for this edge batch."""
      return self._etype
  
file_path: dgl/python/dgl/udf.py
imports: from __future__ import absolute_import
label_desc: Use image node_id 8 for calling the EdgeBatch obj's underlying member method code with example usage: obj.canonical_etype() and returns: self