code: def irfftn(x, s=None, axes=None, norm="backward", name=None):
      x = ivy.array(x)
  
      if axes is None:
          axes = list(range(len(x.shape)))
  
      include_last_axis = len(x.shape) - 1 in axes
  
      if s is None:
          s = [
              x.shape[axis]
              if axis != (len(x.shape) - 1)
              else 2 * (x.shape[axis] - 1)
              for axis in axes
          ]
  
      real_result = x
      remaining_axes = [
          axis for axis in axes if axis != (len(x.shape) - 1)
      ]
  
      if remaining_axes:
          real_result = ivy.ifftn(
              x,
              s=[s[axes.index(axis)] for axis in remaining_axes],
              axes=remaining_axes,
              norm=norm,
          )
  
      if include_last_axis:
          axis = len(x.shape) - 1
          size = s[axes.index(axis)]
          freq_domain = ivy.moveaxis(real_result, axis, -1)
          slices = [slice(None)] * ivy.get_num_dims(freq_domain)
          slices[-1] = slice(0, size // 2 + 1)
          pos_freq_terms = freq_domain[tuple(slices)]
          slices[-1] = slice(1, -1)
          neg_freq_terms = ivy.conj(
              pos_freq_terms[tuple(slices)][..., ::-1]
          )
          combined_freq_terms = ivy.concat(
              (pos_freq_terms, neg_freq_terms), axis=-1
          )
          real_result = ivy.ifftn(
              combined_freq_terms, s=[size], axes=[-1], norm=norm
          )
          real_result = ivy.moveaxis(real_result, -1, axis)
  
      if ivy.is_complex_dtype(x.dtype):
          output_dtype = (
              "float32" if x.dtype == "complex64" else "float64"
          )
      else:
          output_dtype = "float32"
  
      result_t = ivy.astype(real_result, output_dtype)
      return result_t
  
file_path: ivy/ivy/functional/frontends/paddle/fft.py
imports: import ivy
  from ivy.func_wrapper import with_supported_dtypes
  from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back
label_desc: Use image node_id 14 for calling a global function with example usage: irfftn(x, s, axes, norm, name) and returns: result_t