code: def conv3d(
      x: paddle.Tensor,
      filters: paddle.Tensor,
      strides: Union[int, Tuple[int, int, int]],
      padding: Union[str, int, Sequence[Tuple[int, int]]],
      /,
      *,
      data_format: Optional[str] = "NDHWC",
      filter_format: Optional[str] = "channel_last",
      x_dilations: Optional[Union[int, Tuple[int, int, int]]] = 1,
      dilations: Optional[Union[int, Tuple[int, int, int]]] = 1,
      bias: Optional[paddle.Tensor] = None,
      out: Optional[paddle.Tensor] = None,
  ):
      if data_format == "NCDHW":
          x = paddle.transpose(x, perm=(0, 2, 3, 4, 1))
  
      df = "NDHWC"
      x, filters = _ff_xd_before_conv(
          x, filters, 3, filter_format, x_dilations
      )
      x = _pad_before_conv(
          x, filters, strides, padding, 3, dilations, df
      )
      filters = paddle.transpose(filters, perm=(4, 3, 0, 1, 2))
      padding = "VALID"
  
      res = paddle.nn.functional.conv3d(
          x,
          filters,
          bias=bias,
          data_format=df,
          stride=strides,
          padding=padding,
          dilation=dilations,
      )
  
      if data_format == "NCDHW":
          res = paddle.transpose(res, perm=(0, 4, 1, 2, 3))
      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 11 for calling a global function with example usage: conv3d() and returns: res