code: def ifftn(
      x: torch.Tensor,
      s: Optional[Union[int, Tuple[int]]] = None,
      axes: Optional[Union[int, Tuple[int]]] = None,
      *,
      norm: Optional[str] = "backward",
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      return torch.fft.ifftn(x, s=s, dim=axes, norm=norm, out=out)
  
imports: from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import torch
  import math
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _broadcast_pooling_helper
label_desc: Use image node_id 25 for calling a global function with example usage: ifftn(x, s, axes) and returns: torch