code: def _adjust_num_padded_values_to_ceil(
      pad_specific, num_padded_values, x_shape, kernel, strides, dims
  ):
      for i in range(dims):
          pad = [
              pad_specific[i] // 2,
              pad_specific[i] - pad_specific[i] // 2,
          ]
          _, c = _padding_ceil_mode(
              x_shape[i], kernel[i], pad, strides[i], True
          )
          num_padded_values[i][-1] = _add_ceil_pad_to_pad_list(
              num_padded_values[i][-1], kernel[i], c
          )
      return num_padded_values
  
imports: from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import torch
  import math
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _broadcast_pooling_helper
label_desc: Use image node_id 8 for calling a global function with example usage: _adjust_num_padded_values_to_ceil(pad_specific, num_padded_values, x_shape, kernel, strides, dims) and returns: num_padded_values