class_docstr: Embedding layer for featureless heterograph.
class_docstr_tok: ["Embedding","layer","for","featureless","heterograph","."]
docstr: Forward computation
  
  Parameters
  ----------
  block : DGLGraph, optional
      If not specified, directly return the full graph with embeddings stored in
      :attr:`embed_name`. Otherwise, extract and store the embeddings to the block
      graph and return.
  
  Returns
  -------
  DGLGraph
      The block graph fed with embeddings.
docstr_tok: ["Forward","computation","Parameters","--","--","--","--","--","block",":","DGLGraph",",","optional","If","not","specified",",","directly","return","the","full","graph","with","embeddings","stored","in",":","attr",":","`","embed_name","`",".","Otherwise",",","extract","and","store","the","embeddings","to","the","block","graph","and","return",".","Returns","--","--","--","-","DGLGraph","The","block","graph","fed","with","embeddings","."]
code: def forward(self, block=None):
      """Forward computation
  
      Parameters
      ----------
      block : DGLGraph, optional
          If not specified, directly return the full graph with embeddings stored in
          :attr:`embed_name`. Otherwise, extract and store the embeddings to the block
          graph and return.
  
      Returns
      -------
      DGLGraph
          The block graph fed with embeddings.
      """
      return self.embeds
  
imports: from collections import defaultdict
  import dgl
  import dgl.function
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
label_desc: Use image node_id 2 for calling the RelGraphEmbed obj's underlying member method code with example usage: obj.forward(block) and returns: self