code: def _pad_before_conv(
      x,
      filters,
      strides,
      padding,
      dims,
      dilations,
      filter_format="channel_last",
  ):
      dilations = (
          [dilations] * dims
          if isinstance(dilations, int)
          else dilations
      )
      strides = (
          [strides] * dims if isinstance(strides, int) else strides
      )
      filter_shape = (
          filters.shape[2:]
          if filter_format == "channel_first"
          else filters.shape[:dims]
      )
      if isinstance(padding, str):
          # use torch's padding in conv if strides are all 1
          if len(strides) == strides.count(1):
              return x, padding.lower()
          filter_shape = [
              filter_shape[i]
              + (filter_shape[i] - 1) * (dilations[i] - 1)
              for i in range(dims)
          ]
          pad_specific = [
              _handle_padding(
                  x.shape[2 + i], strides[i], filter_shape[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)
          ]
          pad_list = [None] * len(pad_list_top) * 2
          pad_list[::2] = pad_list_top
          pad_list[1::2] = pad_list_bot
      else:
          if isinstance(padding, int):
              return x, padding
          # if symmetric padding is used, use torch's padding in conv function
          if all(pad[0] == pad[1] for pad in padding):
              return x, [pad[0] for pad in padding]
          pad_list = [
              item for sublist in padding for item in sublist[::-1]
          ][::-1]
      return torch.nn.functional.pad(x, pad_list), 0
  
imports: from typing import Optional, Tuple, Union, Sequence
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _get_embed_dim, _handle_padding, _deconv_length
label_desc: Use image node_id 4 for calling a global function with example usage: _pad_before_conv(x, filters, strides, padding, dims, dilations, filter_format) and returns: torch,  int, x,  padding, x,  padding, x,  unknown