code: def _pad_last_dim(
      tensor: torch.Tensor, alignment_size: int, slice: bool
  ) -> torch.Tensor:
      # FlashAttentionV2 requires that head dimension be a multiple of 8
      # This was previously done within the kernel, however
      # This causes the kernel to maybe alias query, key, value
      # So instead we pad the head_dimensions to be a multiple of 8
      # in the composite region
      last_dim_size = tensor.size(-1)
      if last_dim_size % alignment_size == 0:
          return tensor
      pad_count = alignment_size - (last_dim_size % alignment_size)
      tensor = torch.nn.functional.pad(tensor, [0, pad_count])
      if slice:
          return tensor[..., 0:last_dim_size]
      return tensor
  
imports: import logging
  import math
  from typing import Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.backends.cuda import can_use_efficient_attention, can_use_flash_attention, flash_sdp_enabled, math_sdp_enabled, mem_efficient_sdp_enabled, SDPAParams, SDPBackend
  from .nested_tensor import buffer_from_jagged, NestedTensor, ViewNestedFromBuffer
label_desc: Use image node_id 15 for calling a global function with example usage: _pad_last_dim(tensor, alignment_size, slice) and returns: tensor, tensor, tensor