original_docstring: Multi-dimensional Gaussian filter.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar or sequence of scalar): Standard deviations for each axis
              of Gaussian kernel. A single value applies to all axes.
          order (int or sequence of scalar): 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. A
              single value applies to all axes.
          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_filter`
  
      .. 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: Multi-dimensional Gaussian filter.
docstring_tokens: Multi - dimensional Gaussian filter .
code: def gaussian_filter(input, sigma, order=0, output=None, mode="reflect",
                      cval=0.0, truncate=4.0):
      """Multi-dimensional Gaussian filter.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar or sequence of scalar): Standard deviations for each axis
              of Gaussian kernel. A single value applies to all axes.
          order (int or sequence of scalar): 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. A
              single value applies to all axes.
          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_filter`
  
      .. 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.
      """
      sigmas = _util._fix_sequence_arg(sigma, input.ndim, 'sigma', float)
      orders = _util._fix_sequence_arg(order, input.ndim, 'order', int)
      truncate = float(truncate)
  
      def get(param):
          sigma, order = param
          radius = int(truncate * float(sigma) + 0.5)
          if radius <= 0:
              return None
          return _gaussian_kernel1d(sigma, order, radius)
  
      return _run_1d_correlates(input, list(zip(sigmas, orders)), get, output,
                                mode, cval, 0)
code_tokens: def gaussian_filter ( input , sigma , order = 0 , output = None , mode = "reflect" , cval = 0.0 , truncate = 4.0 ) : sigmas = _util . _fix_sequence_arg ( sigma , input . ndim , 'sigma' , float ) orders = _util . _fix_sequence_arg ( order , input . ndim , 'order' , int ) truncate = float ( truncate ) def get ( param ) : sigma , order = param radius = int ( truncate * float ( sigma ) + 0.5 ) if radius <= 0 : return None return _gaussian_kernel1d ( sigma , order , radius ) return _run_1d_correlates ( input , list ( zip ( sigmas , orders ) ) , get , output , mode , cval , 0 )
short_docstring: Multi-dimensional Gaussian filter.
short_docstring_tokens: Multi - dimensional Gaussian filter .
comment: """Multi-dimensional Gaussian filter.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar or sequence of scalar): Standard deviations for each axis
              of Gaussian kernel. A single value applies to all axes.
          order (int or sequence of scalar): 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. A
              single value applies to all axes.
          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_filter`
  
      .. 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.
      """