class_docstr: Embedding layer for featureless heterograph.
class_docstr_tok: ["Embedding","layer","for","featureless","heterograph","."]
code: def __init__(
      self,
      ntype_num,
      embed_size,
  ):
      super(RelGraphEmbed, self).__init__()
      self.embed_size = embed_size
      self.dropout = nn.Dropout(0.0)
  
      # Create weight embeddings for each node for each relation.
      self.embeds = nn.ParameterDict()
      for ntype, num_nodes in ntype_num.items():
          embed = nn.Parameter(th.Tensor(num_nodes, self.embed_size))
          nn.init.xavier_uniform_(
              embed, gain=nn.init.calculate_gain("relu")
          )
          self.embeds[ntype] = embed
  
imports: import argparse
  import time
  import dgl
  import dgl.sparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data.rdf import AIFBDataset, AMDataset, BGSDataset, MUTAGDataset
label_desc: Use image node_id 1 to create a new RelGraphEmbed object from inherited base classes: nn with example: obj = RelGraphEmbed(ntype_num, embed_size)