code: def _dilate_pad_conv_tranpose(
      x, filters, strides, padding, dims, dilations, output_shape
  ):
      strides = (
          [strides] * dims if isinstance(strides, int) else strides
      )
      dilations = (
          [dilations] * dims
          if isinstance(dilations, int)
          else dilations
      )
      if output_shape is None:
          new_shape = [
              _deconv_length(
                  x.shape[i + 1],
                  strides[i],
                  filters.shape[i],
                  padding,
                  dilations[i],
              )
              for i in range(dims)
          ]
          output_shape = [x.shape[0], *new_shape, filters.shape[-1]]
      elif len(output_shape) == dims:
          output_shape = (
              [x.shape[0]] + list(output_shape) + [filters.shape[-1]]
          )
      for i in reversed(range(dims)):
          if strides[i] > 1:
              x = _add_dilations(x, strides[i], axis=i + 1)
          if dilations[i] > 1:
              filters = _add_dilations(filters, dilations[i], axis=i)
      pad_specific = [
          _handle_padding(
              output_shape[i + 1], strides[i], filters.shape[i], padding
          )
          for i in range(dims)
      ]
      extra_pad = [
          max(
              0,
              output_shape[i + 1]
              - (
                  x.shape[i + 1]
                  + filters.shape[i]
                  - 1
                  - pad_specific[i]
              ),
          )
          for i in range(dims)
      ]
      pad_top = [
          filters.shape[i] - 1 - (pad_specific[i] // 2)
          for i in range(dims)
      ]
      pad_bot = [
          filters.shape[i]
          - 1
          - (pad_specific[i] - pad_specific[i] // 2)
          for i in range(dims)
      ]
      pad_list = [
          (pad_top[i], pad_bot[i] + extra_pad[i]) for i in range(dims)
      ]
      x = np.pad(
          x,
          [
              (0, 0),
              *pad_list,
              (0, 0),
          ],
          "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 3 for calling a global function with example usage: _dilate_pad_conv_tranpose(x, filters, strides, padding, dims, dilations, output_shape) and returns: x,  filters