code: def pad_to_multiple(tensor, multiple, dim=-1, value=0):
      seq_len = tensor.shape[dim]
      m = seq_len / multiple
      if m.is_integer():
          return tensor, seq_len
  
      remainder = math.ceil(m) * multiple - seq_len
      pad_offset = (0,) * (-1 - dim) * 2
      padded_tensor = F.pad(
          tensor, (*pad_offset, 0, remainder), value=value
      )
      return padded_tensor, seq_len
  
imports: import math
  import torch
  import torch.nn.functional
  from torch import Tensor, nn, einsum
  from typing import Tuple, Optional
  from einops import rearrange, repeat, reduce, pack, unpack
  from mixture_of_attention.attend import Attend
  from mixture_of_attention.rotary_emb import apply_rotary_pos_emb
  from local_attention import LocalMHA
  from colt5_attention import CoordinateDescentRouter
label_desc: Use image node_id 5 for calling a global function with example usage: pad_to_multiple(tensor, multiple, dim, value) and returns: padded_tensor,  seq_len, tensor,  seq_len