code: def conv_general_transpose(
      x: torch.Tensor,
      filters: torch.Tensor,
      strides: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ],
      padding: str,
      /,
      *,
      dims: int = 2,
      output_shape: Optional[
          Union[ivy.NativeShape, Sequence[int]]
      ] = None,
      filter_format: str = "channel_last",
      data_format: str = "channel_first",
      dilations: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ] = 1,
      feature_group_count: int = 1,
      bias: Optional[torch.Tensor] = None,
      out: Optional[torch.Tensor] = None,
  ):
      if data_format == "channel_last":
          x = x.permute(0, dims + 1, *range(1, dims + 1))
      if filter_format == "channel_last":
          filters = filters.permute(dims + 1, dims, *range(dims))
      (
          not_valid_pad,
          symmetric_padding,
          output_padding,
      ) = _tranpose_padding(
          x.shape[2:],
          filters.shape[2:],
          strides,
          padding,
          dims,
          dilations,
          output_shape,
          data_format,
      )
      if dims == 1:
          res = torch.nn.functional.conv_transpose1d(
              x,
              filters,
              bias,
              strides,
              symmetric_padding,
              dilation=dilations,
              output_padding=output_padding,
              groups=feature_group_count,
          )
          if not_valid_pad[0]:
              res = res[:, :, :-1]
      elif dims == 2:
          res = torch.nn.functional.conv_transpose2d(
              x,
              filters,
              bias,
              strides,
              symmetric_padding,
              dilation=dilations,
              output_padding=output_padding,
              groups=feature_group_count,
          )
          if not_valid_pad[0]:
              res = res[..., :-1, :]
          if not_valid_pad[1]:
              res = res[..., :-1]
      else:
          res = torch.nn.functional.conv_transpose3d(
              x,
              filters,
              bias,
              strides,
              symmetric_padding,
              dilation=dilations,
              output_padding=output_padding,
              groups=feature_group_count,
          )
          if not_valid_pad[0]:
              res = res[..., :-1, :, :]
          if not_valid_pad[1]:
              res = res[..., :, :-1, :]
          if not_valid_pad[2]:
              res = res[..., :, :, :-1]
      if data_format == "channel_last":
          res = res.permute(0, *range(2, dims + 2), 1)
      return res
  
imports: from typing import Optional, Tuple, Union, Sequence
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _get_embed_dim, _handle_padding, _deconv_length
label_desc: Use image node_id 19 for calling a global function with example usage: conv_general_transpose() and returns: res