code: def get_padded_lse(
      self, pad_to: int, force_pad_inf: bool = False
  ) -> torch.Tensor:
      pad_amount = (pad_to - (self.lse.shape[2] % pad_to)) % pad_to
      lse = self.lse
      if pad_amount > 0:
          if force_pad_inf:
              lse = lse[:, :, : self.out.shape[1]]
              pad_amount = (pad_to - (lse.shape[2] % pad_to)) % pad_to
          lse = torch.nn.functional.pad(
              lse, [0, pad_amount], value=math.inf
          )
      elif force_pad_inf and self.out.shape[1] != lse.shape[2]:
          lse[:, :, self.out.shape[1] :].fill_(math.inf)
      return lse
  
imports: import math
  from dataclasses import dataclass
  from functools import partial
  from typing import Any, Callable, List, Mapping, Optional, Set, Tuple, Type, Union
  import torch
  from ..._cpp_lib import _built_with_cuda
  from ..common import BaseOperator
  from .attn_bias import AttentionBias, BlockDiagonalMask, LowerTriangularMask, LowerTriangularMaskWithTensorBias
label_desc: Use image node_id 1 for calling the Context obj's underlying member method code with example usage: obj.get_padded_lse(pad_to, force_pad_inf) and returns: lse