code: def __init__(self, num_embeddings, embedding_dim):
      super(VocabEmbedding, self).__init__()
      # Keep the input dimensions.
      self.num_embeddings = num_embeddings
      self.embedding_dim = embedding_dim
      self.padding_idx = None
      self.max_norm = None
      self.norm_type = 2.0
      self.scale_grad_by_freq = False
      self.sparse = False
      self._weight = None
  
      # Allocate weights and initialize.
      self.weight = nn.Parameter(
          torch.empty(self.num_embeddings, self.embedding_dim)
      )
      init.xavier_uniform_(self.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 1 to create a new VocabEmbedding object from inherited base classes: torch.nn with example: obj = VocabEmbedding(num_embeddings, embedding_dim)