class_docstr: Embedding layer for featureless heterograph.
  Parameters
  ----------
  dev_id : int
      Device to run the layer.
  num_nodes : int
      Number of nodes.
  node_tides : tensor
      Storing the node type id for each node starting from 0
  num_of_ntype : int
      Number of node types
  input_size : list of int
      A list of input feature size for each node type. If None, we then
      treat certain input feature as an one-hot encoding feature.
  embed_size : int
      Output embed size
  dgl_sparse : bool, optional
      If true, use dgl.nn.NodeEmbedding otherwise use torch.nn.Embedding
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code: def dgl_emb(self):
      """ """
      if self.dgl_sparse:
          embs = [emb for emb in self.node_embeds.values()]
          return embs
      else:
          return []
  
file_path: dgl/benchmarks/benchmarks/multigpu/rgcn_model.py
imports: import dgl
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the RelGraphEmbedLayer obj's underlying member method code with example usage: obj.dgl_emb() and returns: embs, list