class_docstr: Gradient wrt. inputs for `CorrMM`.
  
  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 connection_pattern(self, node):
      if node.nin == 2:
          return [[1], [1]]
      else:
          return [[1], [1], [0], [0]]
  
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 int8, 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 5 for calling the CorrMM_gradInputs obj's underlying member method code with example usage: obj.connection_pattern(node) and returns: list, list