parameters: (x, n=None, axis=-1, norm="backward", name=None)
docstring: Calculate one-dimensional discrete Fourier transform.
  
      This function uses the efficient fast Fourier transform (FFT) algorithm [1] to 
      calculate the 1-D * n * point discrete Fourier transform (DFT).
  
      Args:
          x (Tensor): The input data. It's a Tensor type. It's a complex.
          n (int, optional): The length of the output transform axis. If `n` is less than 
              the length input, the input will be cropped. If larger, the input is filled 
              with zeros. If `n` is not given, the input length along the axis specified 
              by `axis` is used.
          axis (int, optional): Axis used to calculate FFT. If not specified, the last axis 
              is used by default.       
          norm (str): Indicates which direction to scale the `forward` or `backward` transform
              pair and what normalization factor to use. The parameter value must be one 
              of "forward" or "backward" or "ortho". Default is "backward", meaning no normalization on
              the forward transforms and scaling by ``1/n`` on the `ifft`. "forward" instead applies 
              the ``1/n`` factor on the forward tranform. For ``norm="ortho"``, both directions are 
              scaled by ``1/sqrt(n)``.
          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:
          complex tensor. The truncated or zero-padded input, transformed along the axis indicated 
          by `axis`, or the last one if `axis` is not specified.
      
      Examples:
  
          .. code-block:: python
  
              import numpy as np
              import paddle
  
              x = np.exp(3j * np.pi * np.arange(7) / 7)
              xp = paddle.to_tensor(x)
              fft_xp = paddle.fft.fft(xp).numpy()
              print(fft_xp)
              #  [1.+1.25396034e+00j 1.+4.38128627e+00j 1.-4.38128627e+00j
              #   1.-1.25396034e+00j 1.-4.81574619e-01j 1.+8.88178420e-16j
              #   1.+4.81574619e-01j]
docstring_summary: Calculate one-dimensional discrete Fourier transform.
docstring_tokens: Calculate one - dimensional discrete Fourier transform .
function: def fft(x, n=None, axis=-1, norm="backward", name=None):
      """
      Calculate one-dimensional discrete Fourier transform.
  
      This function uses the efficient fast Fourier transform (FFT) algorithm [1] to 
      calculate the 1-D * n * point discrete Fourier transform (DFT).
  
      Args:
          x (Tensor): The input data. It's a Tensor type. It's a complex.
          n (int, optional): The length of the output transform axis. If `n` is less than 
              the length input, the input will be cropped. If larger, the input is filled 
              with zeros. If `n` is not given, the input length along the axis specified 
              by `axis` is used.
          axis (int, optional): Axis used to calculate FFT. If not specified, the last axis 
              is used by default.       
          norm (str): Indicates which direction to scale the `forward` or `backward` transform
              pair and what normalization factor to use. The parameter value must be one 
              of "forward" or "backward" or "ortho". Default is "backward", meaning no normalization on
              the forward transforms and scaling by ``1/n`` on the `ifft`. "forward" instead applies 
              the ``1/n`` factor on the forward tranform. For ``norm="ortho"``, both directions are 
              scaled by ``1/sqrt(n)``.
          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:
          complex tensor. The truncated or zero-padded input, transformed along the axis indicated 
          by `axis`, or the last one if `axis` is not specified.
      
      Examples:
  
          .. code-block:: python
  
              import numpy as np
              import paddle
  
              x = np.exp(3j * np.pi * np.arange(7) / 7)
              xp = paddle.to_tensor(x)
              fft_xp = paddle.fft.fft(xp).numpy()
              print(fft_xp)
              #  [1.+1.25396034e+00j 1.+4.38128627e+00j 1.-4.38128627e+00j
              #   1.-1.25396034e+00j 1.-4.81574619e-01j 1.+8.88178420e-16j
              #   1.+4.81574619e-01j]
  
  
      """
      if is_integer(x) or is_floating_point(x):
          return fft_r2c(
              x, n, axis, norm, forward=True, onesided=False, name=name)
      else:
          return fft_c2c(x, n, axis, norm, forward=True, name=name)
function_tokens: def fft ( x , n = None , axis = - 1 , norm = "backward" , name = None ) : if is_integer ( x ) or is_floating_point ( x ) : return fft_r2c ( x , n , axis , norm , forward = True , onesided = False , name = name ) else : return fft_c2c ( x , n , axis , norm , forward = True , name = name )