class_docstr: Gradient wrt. filters for `Corr3dMM`.
  
  Notes
  -----
  You will not want to use this directly, but rely on
  PyTensor's automatic differentiation or graph optimization to
  use it as needed.
code: def c_code(self, node, nodename, inp, out_, sub):
      bottom, top = inp[:2]
      height, width, depth = inp[2:] or (None, None, None)
      (weights,) = out_
      return super().c_code_helper(
          bottom, weights, top, sub, height, width, depth
      )
  
imports: import logging
  import os
  from typing import Optional
  import pytensor
  from pytensor.configdefaults import config
  from pytensor.graph.basic import Apply
  from pytensor.graph.op import _NoPythonOp
  from pytensor.link.c.op import OpenMPOp
  from pytensor.link.c.params_type import ParamsType
  from pytensor.link.c.type import EnumList
  from pytensor.scalar import int64
  from pytensor.tensor import blas_headers
  from pytensor.tensor.basic import as_tensor_variable
  from pytensor.tensor.blas import blas_header_version, ldflags
  from pytensor.tensor.conv.abstract_conv import get_conv_output_shape
  from pytensor.tensor.type import TensorType
label_desc: Use image node_id 3 for calling the Corr3dMMGradWeights obj's underlying member method code with example usage: obj.c_code(node, nodename, inp, out_, sub) and returns: super