class_docstr: Gradient wrt. filters for `Corr3dMM`.
  
  Notes
  -----
  You will not want to use this directly, but rely on
  Theano's automatic differentiation or graph optimization to
  use it as needed.
class_docstr_tok: ["Gradient","wrt",".","filters","for","`","Corr3dMM","`",".","Notes","--","--","-","You","will","not","want","to","use","this","directly",",","but","rely","on","Theano","'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(Corr3dMM_gradWeights, self).c_code_helper(
          bottom, weights, top, sub, height, width, depth
      )
  
imports: from __future__ import absolute_import, print_function, division
  import os
  import logging
  from six import integer_types
  import theano
  from theano import Apply
  from theano import gof
  from theano.gof import ParamsType, EnumList
  from theano.scalar import int64
  from theano.tensor import as_tensor_variable, TensorType
  from theano.tensor.nnet.abstract_conv import get_conv_output_shape
  from theano.tensor import blas_headers
  from theano.tensor.blas import ldflags, blas_header_version
label_desc: Use image node_id 3 for calling the Corr3dMM_gradWeights obj's underlying member method code with example usage: obj.c_code(node, nodename, inp, out_, sub) and returns: super