code: def forward(self, x: torch.Tensor):
      position_ids = torch.arange(
          x.shape[1], dtype=torch.long, device=x.device
      )[None, :].repeat(x.shape[0], 1)
  
      X_token = self.word_embeddings(x)
      X_pos = self.position_embeddings(position_ids)
  
      X = X_token + X_pos
      X = self.dropout(X)
  
      return X
  
file_path: xformers/xformers/components/positional_embedding/vocab.py
imports: from dataclasses import dataclass
  from typing import Optional
  import torch
  import torch.nn
  from xformers.components.positional_embedding import PositionEmbedding, PositionEmbeddingConfig, register_positional_embedding
label_desc: Use image node_id 3 for calling the VocabEmbedding obj's underlying member method code with example usage: obj.forward(x) and returns: X