original_docstring: Multi-dimensional correlate.
  
      The array is correlated with the given kernel.
  
      Args:
          input (cupy.ndarray): The input array.
          weights (cupy.ndarray): Array of weights, same number of dimensions as
              input
          output (cupy.ndarray, dtype or None): The array in which to place the
              output.
          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``.
          origin (scalar or tuple of scalar): The origin parameter controls the
              placement of the filter, relative to the center of the current
              element of the input. Default of 0 is equivalent to
              ``(0,)*input.ndim``.
  
      Returns:
          cupy.ndarray: The result of correlate.
  
      .. seealso:: :func:`scipy.ndimage.correlate`
  
      .. 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 correlate.
  The array is correlated with the given kernel.
docstring_tokens: Multi - dimensional correlate . The array is correlated with the given kernel .
code: def correlate(input, weights, output=None, mode='reflect', cval=0.0, origin=0):
      """Multi-dimensional correlate.
  
      The array is correlated with the given kernel.
  
      Args:
          input (cupy.ndarray): The input array.
          weights (cupy.ndarray): Array of weights, same number of dimensions as
              input
          output (cupy.ndarray, dtype or None): The array in which to place the
              output.
          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``.
          origin (scalar or tuple of scalar): The origin parameter controls the
              placement of the filter, relative to the center of the current
              element of the input. Default of 0 is equivalent to
              ``(0,)*input.ndim``.
  
      Returns:
          cupy.ndarray: The result of correlate.
  
      .. seealso:: :func:`scipy.ndimage.correlate`
  
      .. 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.
      """
      return _correlate_or_convolve(input, weights, output, mode, cval, origin)
code_tokens: def correlate ( input , weights , output = None , mode = 'reflect' , cval = 0.0 , origin = 0 ) : return _correlate_or_convolve ( input , weights , output , mode , cval , origin )
short_docstring: Multi-dimensional correlate.
short_docstring_tokens: Multi - dimensional correlate .
comment: """Multi-dimensional correlate.
  
      The array is correlated with the given kernel.
  
      Args:
          input (cupy.ndarray): The input array.
          weights (cupy.ndarray): Array of weights, same number of dimensions as
              input
          output (cupy.ndarray, dtype or None): The array in which to place the
              output.
          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``.
          origin (scalar or tuple of scalar): The origin parameter controls the
              placement of the filter, relative to the center of the current
              element of the input. Default of 0 is equivalent to
              ``(0,)*input.ndim``.
  
      Returns:
          cupy.ndarray: The result of correlate.
  
      .. seealso:: :func:`scipy.ndimage.correlate`
  
      .. 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.
      """