code: def conv3d_transpose(
      x: torch.Tensor,
      filters: torch.Tensor,
      strides: Union[int, Tuple[int, int, int]],
      padding: str,
      /,
      *,
      output_shape: Optional[
          Union[ivy.NativeShape, Sequence[int]]
      ] = None,
      filter_format: str = "channel_last",
      data_format: str = "NDHWC",
      dilations: Union[int, Tuple[int, int, int]] = 1,
      bias: Optional[torch.Tensor] = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      if data_format == "NDHWC":
          x = x.permute(0, 4, 1, 2, 3)
      if filter_format == "channel_last":
          filters = filters.permute(4, 3, 0, 1, 2)
      (
          not_valid_pad,
          symmetric_padding,
          output_padding,
      ) = _tranpose_padding(
          x.shape[2:],
          filters.shape[2:],
          strides,
          padding,
          3,
          dilations,
          output_shape,
          data_format,
      )
      res = torch.nn.functional.conv_transpose3d(
          x,
          filters,
          bias,
          strides,
          symmetric_padding,
          dilation=dilations,
          output_padding=output_padding,
      )
      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 == "NDHWC":
          res = res.permute(0, 2, 3, 4, 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 16 for calling a global function with example usage: conv3d_transpose() and returns: res