code: def forward(self, inputs, mask: Optional[torch.Tensor] = None):
      positions_hidden = self.position_embed(self.positions)
      token_hidden = self.token_embed(inputs)
      return token_hidden + positions_hidden
  
imports: import logging
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch import nn
  from ludwig.constants import TYPE
  from ludwig.modules.initializer_modules import get_initializer
  from ludwig.utils.data_utils import load_pretrained_embeddings
  from ludwig.utils.torch_utils import get_torch_device, LudwigModule
label_desc: Use image node_id 4 for calling the TokenAndPositionEmbedding obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: unknown