class_docstr: Language model embeddings.
  Arguments:
      hidden_size: hidden size
      vocab_size: vocabulary size
      max_sequence_length: maximum size of sequence. This
                           is used for positional embedding
      embedding_dropout_prob: dropout probability for embeddings
      init_method: weight initialization method
      num_tokentypes: size of the token-type embeddings. 0 value
                      will ignore this embedding
class_docstr_tok: ["Language","model","embeddings",".","Arguments",":","hidden_size",":","hidden","size","vocab_size",":","vocabulary","size","max_sequence_length",":","maximum","size","of","sequence",".","This","is","used","for","positional","embedding","embedding_dropout_prob",":","dropout","probability","for","embeddings","init_method",":","weight","initialization","method","num_tokentypes",":","size","of","the","token-type","embeddings",".","0","value","will","ignore","this","embedding"]
code: def word_embedding_weight(self):
      return self.word_embeddings.weight
  
file_path: ColossalAI/examples/tutorial/sequence_parallel/model/layers/embedding.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
label_desc: Use image node_id 2 for calling the Embedding obj's underlying member method code with example usage: obj.word_embedding_weight() and returns: self