original_docstring: One-dimensional Gaussian filter along the given axis.
  
      The lines of the array along the given axis are filtered with a Gaussian
      filter of the given standard deviation.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar): Standard deviation for Gaussian kernel.
          axis (int): The axis of input along which to calculate. Default is -1.
          order (int): An order of ``0``, the default, corresponds to convolution
              with a Gaussian kernel. A positive order corresponds to convolution
              with that derivative of a Gaussian.
          output (cupy.ndarray, dtype or None): The array in which to place the
              output. Default is is same dtype as the input.
          mode (str): The array borders are handled according to the given mode
              (``'reflect'``, ``'constant'``, ``'nearest'``, ``'mirror'``,
              ``'wrap'``). Default is ``'reflect'``.
          cval (scalar): Value to fill past edges of input if mode is
              ``'constant'``. Default is ``0.0``.
          truncate (float): Truncate the filter at this many standard deviations.
              Default is ``4.0``.
  
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.gaussian_filter1d`
  
      .. note::
          When the output data type is integral (or when no output is provided
          and input is integral) the results may not perfectly match the results
          from SciPy due to floating-point rounding of intermediate results.
      
docstring: One-dimensional Gaussian filter along the given axis.
  The lines of the array along the given axis are filtered with a Gaussian
  filter of the given standard deviation.
docstring_tokens: One - dimensional Gaussian filter along the given axis . The lines of the array along the given axis are filtered with a Gaussian filter of the given standard deviation .
code: def gaussian_filter1d(input, sigma, axis=-1, order=0, output=None,
                        mode="reflect", cval=0.0, truncate=4.0):
      """One-dimensional Gaussian filter along the given axis.
  
      The lines of the array along the given axis are filtered with a Gaussian
      filter of the given standard deviation.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar): Standard deviation for Gaussian kernel.
          axis (int): The axis of input along which to calculate. Default is -1.
          order (int): An order of ``0``, the default, corresponds to convolution
              with a Gaussian kernel. A positive order corresponds to convolution
              with that derivative of a Gaussian.
          output (cupy.ndarray, dtype or None): The array in which to place the
              output. Default is is same dtype as the input.
          mode (str): The array borders are handled according to the given mode
              (``'reflect'``, ``'constant'``, ``'nearest'``, ``'mirror'``,
              ``'wrap'``). Default is ``'reflect'``.
          cval (scalar): Value to fill past edges of input if mode is
              ``'constant'``. Default is ``0.0``.
          truncate (float): Truncate the filter at this many standard deviations.
              Default is ``4.0``.
  
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.gaussian_filter1d`
  
      .. note::
          When the output data type is integral (or when no output is provided
          and input is integral) the results may not perfectly match the results
          from SciPy due to floating-point rounding of intermediate results.
      """
      radius = int(float(truncate) * float(sigma) + 0.5)
      weights = _gaussian_kernel1d(sigma, int(order), radius)
      return correlate1d(input, weights, axis, output, mode, cval)
code_tokens: def gaussian_filter1d ( input , sigma , axis = - 1 , order = 0 , output = None , mode = "reflect" , cval = 0.0 , truncate = 4.0 ) : radius = int ( float ( truncate ) * float ( sigma ) + 0.5 ) weights = _gaussian_kernel1d ( sigma , int ( order ) , radius ) return correlate1d ( input , weights , axis , output , mode , cval )
short_docstring: One-dimensional Gaussian filter along the given axis.
short_docstring_tokens: One - dimensional Gaussian filter along the given axis .
comment: """One-dimensional Gaussian filter along the given axis.
  
      The lines of the array along the given axis are filtered with a Gaussian
      filter of the given standard deviation.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar): Standard deviation for Gaussian kernel.
          axis (int): The axis of input along which to calculate. Default is -1.
          order (int): An order of ``0``, the default, corresponds to convolution
              with a Gaussian kernel. A positive order corresponds to convolution
              with that derivative of a Gaussian.
          output (cupy.ndarray, dtype or None): The array in which to place the
              output. Default is is same dtype as the input.
          mode (str): The array borders are handled according to the given mode
              (``'reflect'``, ``'constant'``, ``'nearest'``, ``'mirror'``,
              ``'wrap'``). Default is ``'reflect'``.
          cval (scalar): Value to fill past edges of input if mode is
              ``'constant'``. Default is ``0.0``.
          truncate (float): Truncate the filter at this many standard deviations.
              Default is ``4.0``.
  
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.gaussian_filter1d`
  
      .. note::
          When the output data type is integral (or when no output is provided
          and input is integral) the results may not perfectly match the results
          from SciPy due to floating-point rounding of intermediate results.
      """