code: def _dilate_pad_conv(x, filters, strides, padding, dims, dilations):
      for j in range(dims):
          if dilations[j] > 1:
              filters = _add_dilations(filters, dilations[j], axis=j)
      if isinstance(padding, str):
          pad_specific = [
              _handle_padding(
                  x.shape[1 + i], strides[i], filters.shape[i], padding
              )
              for i in range(dims)
          ]
          pad_list = [
              (
                  pad_specific[i] // 2,
                  pad_specific[i] - pad_specific[i] // 2,
              )
              for i in range(dims)
          ]
      elif isinstance(padding, int):
          pad_list = [(padding, padding)] * dims
      else:
          pad_list = [
              (_p, _p) if isinstance(_p, int) else _p for _p in padding
          ]
  
      pad_width = [(0, 0), *pad_list, (0, 0)]
  
      x = np.pad(
          x,
          pad_width=pad_width,
          mode="constant",
      )
      return x, filters
  
file_path: ivy/ivy/functional/backends/numpy/layers.py
imports: import numpy
  from typing import Union, Tuple, Optional, Sequence
  import ivy
  from ivy.functional.ivy.layers import _handle_padding, _deconv_length, _get_x_data_format
label_desc: Use image node_id 2 for calling a global function with example usage: _dilate_pad_conv(x, filters, strides, padding, dims, dilations) and returns: x,  filters