code: def _pad_before_conv_tranpose(
      x,
      filters,
      strides,
      padding,
      dims,
      dilations,
      output_shape,
      filter_shape,
  ):
      if output_shape is None:
          out_shape = [
              _deconv_length(
                  x.shape[i + 2],
                  strides[i],
                  filter_shape[i],
                  padding,
                  dilations[i],
              )
              for i in range(dims)
          ]
          output_shape = [x.shape[0], *out_shape, filters.shape[1]]
      elif len(output_shape) == dims:
          output_shape = (
              [x.shape[0]] + output_shape + [filters.shape[1]]
          )
      not_valid_pad = [False] * dims
      filter_shape = [
          filter_shape[i] + (filter_shape[i] - 1) * (dilations[i] - 1)
          for i in range(dims)
      ]
      pad_specific = [
          _handle_padding(
              output_shape[i + 1], strides[i], filter_shape[i], padding
          )
          for i in range(dims)
      ]
      if padding == "VALID":
          padding_list = [0] * dims
      else:
          for i in range(dims):
              if pad_specific[i] % 2 != 0:
                  pad_specific[i] -= 1
                  not_valid_pad[i] = True
          padding_list = [pad_specific[i] // 2 for i in range(dims)]
      out_shape = [
          (x.shape[i + 2] - 1) * strides[i]
          - 2 * padding_list[i]
          + dilations[i] * (filters.shape[i + 2] - 1)
          + 1
          for i in range(dims)
      ]
      output_padding = [
          max(output_shape[i + 1] - out_shape[i], 0)
          for i in range(dims)
      ]
      return not_valid_pad, padding_list, output_padding
  
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 4 for calling a global function with example usage: _pad_before_conv_tranpose(x, filters, strides, padding, dims, dilations, output_shape, filter_shape) and returns: not_valid_pad,  padding_list,  output_padding