parameters: (x, s=None, axes=None, norm="backward", name=None)
return_statement: return fftn_r2c(x, s, axes, norm, forward=False, onesided=True, name=name)
docstring: The n dimensional inverse FFT of a signal that has Hermitian symmetry.
  
      This function computes the n dimensional inverse FFT over any number of axes 
      in an M-dimensional of a signal that has Hermitian symmetry by means of an 
      efficient algorithm called the Fast Fourier Transform (FFT).
  
      Args:
          x(Tensor): Input tensor.
          s(Sequence[int], optional) : Shape (length along each transformed axis) 
              to use from the input. (``s[0]`` refers to axis 0, ``s[1]`` to axis 
              1, etc.). Along any axis, if the given shape is smaller than that 
              of the input, the input is cropped. If it is larger, the input is 
              padded with zeros. if `s` is not given, the shape of the input 
              along the axes specified by `axes` is used.
          axis(Sequence[int], optional) : Axis over which to compute the inverse FFT. If not
              given, the last axis is used.
          norm(str, optional) : Normalization mode, indicates which direction of 
              the forward/backward pair of transforms is scaled and with what 
              normalization factor. Include {"backward", "ortho", "forward"}, 
              default value is "backward".
          name(str, optional): The default value is None.  Normally there is no 
              need for user to set this property. For more information, please 
              refer to :ref:`api_guide_Name` . 
  
      Returns:
          out(Tensor) : complex tensor.
  
      Examples:
      .. code-block:: python
          import paddle 
  
          spectrum = paddle.to_tensor([10.0, -5.0, 0.0, -1.0, 0.0, -5.0])
          print(paddle.fft.ifft(spectrum))
          # Tensor(shape=[6], dtype=complex64, place=CUDAPlace(0), stop_gradient=True,
          #       [(-0.1666666716337204+0j),  (1-1.9868215517249155e-08j), (2.3333334922790527-1.9868215517249155e-08j),  (3.5+0j), (2.3333334922790527+1.9868215517249155e-08j),  (1+1.9868215517249155e-08j)])
          print(paddle.fft.ihfft(spectrum))
          #  Tensor(shape = [4], dtype = complex64, place = CUDAPlace(0), stop_gradient = True,
          #         [(-0.1666666716337204+0j),  (1-1.9868215517249155e-08j), (2.3333334922790527-1.9868215517249155e-08j),  (3.5+0j)])
docstring_summary: The n dimensional inverse FFT of a signal that has Hermitian symmetry.
docstring_tokens: The n dimensional inverse FFT of a signal that has Hermitian symmetry .
function: def ihfftn(x, s=None, axes=None, norm="backward", name=None):
      """
      The n dimensional inverse FFT of a signal that has Hermitian symmetry.
  
      This function computes the n dimensional inverse FFT over any number of axes 
      in an M-dimensional of a signal that has Hermitian symmetry by means of an 
      efficient algorithm called the Fast Fourier Transform (FFT).
  
      Args:
          x(Tensor): Input tensor.
          s(Sequence[int], optional) : Shape (length along each transformed axis) 
              to use from the input. (``s[0]`` refers to axis 0, ``s[1]`` to axis 
              1, etc.). Along any axis, if the given shape is smaller than that 
              of the input, the input is cropped. If it is larger, the input is 
              padded with zeros. if `s` is not given, the shape of the input 
              along the axes specified by `axes` is used.
          axis(Sequence[int], optional) : Axis over which to compute the inverse FFT. If not
              given, the last axis is used.
          norm(str, optional) : Normalization mode, indicates which direction of 
              the forward/backward pair of transforms is scaled and with what 
              normalization factor. Include {"backward", "ortho", "forward"}, 
              default value is "backward".
          name(str, optional): The default value is None.  Normally there is no 
              need for user to set this property. For more information, please 
              refer to :ref:`api_guide_Name` . 
  
      Returns:
          out(Tensor) : complex tensor.
  
      Examples:
      .. code-block:: python
          import paddle 
  
          spectrum = paddle.to_tensor([10.0, -5.0, 0.0, -1.0, 0.0, -5.0])
          print(paddle.fft.ifft(spectrum))
          # Tensor(shape=[6], dtype=complex64, place=CUDAPlace(0), stop_gradient=True,
          #       [(-0.1666666716337204+0j),  (1-1.9868215517249155e-08j), (2.3333334922790527-1.9868215517249155e-08j),  (3.5+0j), (2.3333334922790527+1.9868215517249155e-08j),  (1+1.9868215517249155e-08j)])
          print(paddle.fft.ihfft(spectrum))
          #  Tensor(shape = [4], dtype = complex64, place = CUDAPlace(0), stop_gradient = True,
          #         [(-0.1666666716337204+0j),  (1-1.9868215517249155e-08j), (2.3333334922790527-1.9868215517249155e-08j),  (3.5+0j)])
  
      """
      return fftn_r2c(x, s, axes, norm, forward=False, onesided=True, name=name)
function_tokens: def ihfftn ( x , s = None , axes = None , norm = "backward" , name = None ) : return fftn_r2c ( x , s , axes , norm , forward = False , onesided = True , name = name )