code: def _get_specific_pad(x_shape, kernel, strides, padding, dims):
      if isinstance(padding, str):
          if padding == "SAME":
              pad_specific = [
                  _handle_padding(
                      x_shape[i], strides[i], kernel[i], padding
                  )
                  for i in range(dims - 1, -1, -1)
              ]
              pad_list_top = [pad_specific[i] // 2 for i in range(dims)]
              pad_list_bot = [
                  pad_specific[i] - pad_specific[i] // 2
                  for i in range(dims)
              ]
              padding = [None] * len(pad_list_top) * 2
              padding[::2] = pad_list_top
              padding[1::2] = pad_list_bot
              pad_specific = pad_specific[::-1]
          else:
              pad_specific = [0] * dims
              padding = [0] * dims * 2
      else:
          if isinstance(padding, int):
              padding = [(padding, padding)] * dims
          pad_specific = [sum(padding[i]) for i in range(dims)]
          padding = [
              item for sublist in padding for item in sublist[::-1]
          ][::-1]
      return padding, pad_specific
  
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 6 for calling a global function with example usage: _get_specific_pad(x_shape, kernel, strides, padding, dims) and returns: padding,  pad_specific