code: def _apply_embedding(
      self,
      cont: Optional[Tensor],
      cont_emb: Tensor,
      cont_bias: Tensor,
  ):
      if cont is not None:
          # the line below is equivalent to following einsums
          # e_cont = torch.einsum('btf,fh->bthf', cont, cont_emb)
          # e_cont = torch.einsum('bf,fh->bhf', cont, cont_emb)
          e_cont = torch.mul(cont.unsqueeze(-1), cont_emb)
          e_cont = e_cont + cont_bias
          return e_cont
  
      return None
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import LayerNorm
  import logging
  import warnings
  from typing import Tuple, Optional
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 2 for calling the TFTEmbedding obj's underlying member method code with example usage: obj._apply_embedding(cont, cont_emb, cont_bias) and returns: None, e_cont