code: def conv_general_transpose(
      x: paddle.Tensor,
      filters: paddle.Tensor,
      strides: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ],
      padding: Union[str, Sequence[Tuple[int, int]]],
      /,
      *,
      dims: Optional[int] = 2,
      output_shape: Optional[
          Union[ivy.NativeShape, Sequence[int]]
      ] = None,
      filter_format: str = "channel_last",
      data_format: Optional[str] = "NDHWC",
      dilations: Optional[
          Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]]
      ] = 1,
      feature_group_count: Optional[int] = 1,
      bias: Optional[paddle.Tensor] = None,
      out: Optional[paddle.Tensor] = None,
  ):
      if data_format == "channel_last":
          x = x.transpose([0, dims + 1, *range(1, dims + 1)])
      if filter_format == "channel_last":
          filters = filters.transpose([dims + 1, dims, *range(dims)])
      strides = (
          [strides] * dims if isinstance(strides, int) else strides
      )
      dilations = (
          [dilations] * dims
          if isinstance(dilations, int)
          else dilations
      )
      (
          not_valid_pad,
          padding_list,
          output_padding,
      ) = _pad_before_conv_tranpose(
          x,
          filters,
          strides,
          padding,
          dims,
          dilations,
          output_shape,
          filters.shape[2:],
      )
      if dims == 1:
          res = paddle.nn.functional.conv1d_transpose(
              x,
              filters,
              bias=bias,
              stride=strides,
              padding=padding_list,
              output_padding=output_padding,
              groups=feature_group_count,
              dilation=dilations,
              data_format="NCL",
          )
          if not_valid_pad[0]:
              res = res[:, :, 0:-1]
      elif dims == 2:
          res = paddle.nn.functional.conv2d_transpose(
              x,
              filters,
              bias=bias,
              stride=strides,
              padding=padding_list,
              output_padding=output_padding,
              groups=feature_group_count,
              dilation=dilations,
              data_format="NCHW",
          )
          if not_valid_pad[0]:
              res = res[:, :, 0:-1, :]
          if not_valid_pad[1]:
              res = res[:, :, :, 0:-1]
      else:
          res = paddle.nn.functional.conv3d_transpose(
              x,
              filters,
              bias=bias,
              stride=strides,
              padding=padding_list,
              output_padding=output_padding,
              groups=feature_group_count,
              dilation=dilations,
              data_format="NCDHW",
          )
          if not_valid_pad[0]:
              res = res[:, 0:-1, :, :]
          if not_valid_pad[1]:
              res = res[:, :, 0:-1, :]
          if not_valid_pad[2]:
              res = res[:, :, :, 0:-1]
      if data_format == "channel_last":
          res = res.transpose([0, *range(2, dims + 2), 1])
      return res
  
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 14 for calling a global function with example usage: conv_general_transpose() and returns: res