code: def pad_bmm(
      mat1: Tensor,
      mat2: Tensor,
      m_padded_length: int,
      k_padded_length: int,
      n_padded_length: int,
  ) -> Tensor:
      # bmm_replace will go through pad_bmm multiple times if multiple dimensions are needed to be padded
      if k_padded_length != 0:
          mat1 = pad_dim(mat1, k_padded_length, 2)
          mat2 = pad_dim(mat2, k_padded_length, 1)
  
          return aten.bmm(mat1, mat2)
      elif n_padded_length != 0:
          mat2 = pad_dim(mat2, n_padded_length, 2)
          return aten.bmm(mat1, mat2)[
              :, :, :-n_padded_length
          ].contiguous()
      else:
          mat1 = pad_dim(mat1, m_padded_length, 1)
          return aten.bmm(mat1, mat2)[
              :, :-m_padded_length, :
          ].contiguous()
  
imports: import functools
  from typing import List, Optional
  import torch
  from torch import Tensor
  from torch._inductor import utils
  from torch.utils._mode_utils import no_dispatch
  from torch.utils._triton import has_triton
  from ..pattern_matcher import fwd_only, joint_fwd_bwd, Match, register_replacement
label_desc: Use image node_id 26 for calling a global function with example usage: pad_bmm(mat1, mat2, m_padded_length, k_padded_length, n_padded_length) and returns: aten, aten, aten