code: def ifftn(
      x: Union[tf.Tensor, tf.Variable],
      s: Optional[Union[int, Tuple[int]]] = None,
      axes: Optional[Union[int, Tuple[int]]] = None,
      *,
      norm: Optional[str] = "backward",
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      result = _ifftn_helper(x, s, axes, norm)
  
      if out is not None:
          out = result
          return out
      else:
          return result
  
imports: import math
  from typing import Union, Optional, Tuple, List, Literal, Sequence, Callable
  import tensorflow
  from ivy.func_wrapper import inputs_to_ivy_arrays, output_to_native_arrays, with_unsupported_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from ..None import backend_version
  import ivy
  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, _get_size
label_desc: Use image node_id 41 for calling a global function with example usage: ifftn(x, s, axes) and returns: out, result