code: def conv3d_v_1p9p0_and_above(
      x: torch.Tensor,
      filters: torch.Tensor,
      strides: Union[int, Tuple[int, int, int]],
      padding: Union[str, int, Sequence[Tuple[int, int]]],
      /,
      *,
      data_format: str = "NDHWC",
      filter_format: str = "channel_last",
      x_dilations: Union[int, Tuple[int, int, int]] = 1,
      dilations: Union[int, Tuple[int, int, int]] = 1,
      bias: Optional[torch.Tensor] = None,
      out: Optional[torch.Tensor] = None,
  ):
      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)
      x = _x_dil_before_conv(x, 3, x_dilations)
      if padding != "SAME" or all(
          s == 1
          for s in ([strides] if isinstance(strides, int) else strides)
      ):
          x, padding = _new_pad_before_conv(x, padding)
      else:
          x, padding = _pad_before_conv(
              x,
              filters,
              strides,
              padding,
              3,
              dilations,
              "channel_first",
          )
      res = torch.nn.functional.conv3d(
          x, filters, bias, strides, padding, dilations
      )
      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 15 for calling a global function with example usage: conv3d_v_1p9p0_and_above() and returns: res