code: def rfft(
      x: torch.Tensor,
      /,
      *,
      n: Optional[int] = None,
      axis: int = -1,
      norm: Literal["backward", "ortho", "forward"] = "backward",
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      x = x.real
      if x.dtype == torch.float16:
          x = x.to(torch.float32)
  
      ret = torch.fft.rfft(x, n=n, dim=axis, norm=norm)
  
      if ivy.exists(out):
          return ivy.inplace_update(out, ret)
      return ret
  
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 26 for calling a global function with example usage: rfft() and returns: ret, ivy