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)
      weights_dtype = _util._init_weights_dtype(input)
  
      def get(param):
          sigma, order = param
          radius = int(truncate * float(sigma) + 0.5)
          if radius <= 0:
              return None
          return _gaussian_kernel1d(
              sigma, order, radius, dtype=weights_dtype
          )
  
      return _run_1d_correlates(
          input, list(zip(sigmas, orders)), get, output, mode, cval, 0
      )
  
imports: import numpy
  import cupy
  from cupy import _core
  from cupy._core import internal
  from cupyx.scipy.ndimage import _util
  from cupyx.scipy.ndimage import _filters_core
  from cupyx.scipy.ndimage import _filters_generic
label_desc: Use image node_id 11 for calling a global function with example usage: gaussian_filter(input, sigma, order, output, mode, cval, truncate) and returns: _run_1d_correlates, _gaussian_kernel1d, None