code: def _correlate_or_convolve(
      input, weights, output, mode, cval, origin, convolution=False
  ):
      origins, int_type = _filters_core._check_nd_args(
          input, weights, mode, origin
      )
      if weights.size == 0:
          return cupy.zeros_like(input)
  
      _util._check_cval(
          mode, cval, _util._is_integer_output(output, input)
      )
  
      if convolution:
          weights = weights[
              tuple([slice(None, None, -1)] * weights.ndim)
          ]
          origins = list(origins)
          for i, wsize in enumerate(weights.shape):
              origins[i] = -origins[i]
              if wsize % 2 == 0:
                  origins[i] -= 1
          origins = tuple(origins)
      elif weights.dtype.kind == "c":
          # numpy.correlate conjugates weights rather than input.
          weights = weights.conj()
      weights_dtype = _util._get_weights_dtype(input, weights)
      offsets = _filters_core._origins_to_offsets(
          origins, weights.shape
      )
      kernel = _get_correlate_kernel(
          mode, weights.shape, int_type, offsets, cval
      )
      output = _filters_core._call_kernel(
          kernel, input, weights, output, weights_dtype=weights_dtype
      )
      return output
  
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 5 for calling a global function with example usage: _correlate_or_convolve(input, weights, output, mode, cval, origin, convolution) and returns: output, cupy