code: def _ff_xd_before_conv(x, filters, dims, filter_format, x_dilations):
      if filter_format == "channel_first":
          filters = paddle.transpose(
              filters, (*range(2, dims + 2), 1, 0)
          )
  
      # adding dilation in input
      x_dilations = (
          [x_dilations] * dims
          if isinstance(x_dilations, int)
          else x_dilations
      )
      for i in range(dims):
          if x_dilations[i] > 1:
              h = x.shape[1 + i]
              new_height = h + (h - 1) * (x_dilations[i] - 1)
              h = paddle.eye(new_height, dtype=x.dtype)[
                  :: x_dilations[i]
              ]
              x = paddle_backend.swapaxes(x, 1 + i, -1)
              x = paddle.matmul(x, h)
              x = paddle_backend.swapaxes(x, -1, 1 + i)
      return x, filters
  
imports: from typing import Optional, Tuple, Union, Sequence
  import paddle
  import ivy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes
  from ivy.utils.exceptions import IvyNotImplementedException
  from ivy.functional.ivy.layers import _handle_padding, _get_x_data_format, _deconv_length
  import ivy.functional.backends.paddle
  from .None import backend_version
label_desc: Use image node_id 5 for calling a global function with example usage: _ff_xd_before_conv(x, filters, dims, filter_format, x_dilations) and returns: x,  filters