code: def forward(
      self, inputs: torch.Tensor, mask: Optional[torch.Tensor] = None
  ):
      if inputs.dtype not in [torch.int, torch.long]:
          raise RuntimeError(
              f"Expected tensor of type torch.int or torch.long as input."
              f"Received {inputs.dtype} instead."
          )
  
      embedded = self.embeddings(inputs)
      if self.dropout:
          embedded = self.dropout(embedded)
      return embedded
  
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 2 for calling the EmbedSequence obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: embedded