code: def binary_convolution(
      filter_shape,
      num_filters=1,
      channels=1,
      init=C.glorot_uniform(),
      pad=False,
      strides=1,
      name="BinaryConvolution",
  ):
      """
      Creates a binary convolution function based on the input parameters.
  
      Args:
          filter_shape : shape of the filter
          num_filters  : number of filters to use
          init         : initialization function for the filter
          pad          : padding enabled or not for the filter
          strides      : overlap for this filter
          name         : name given to the binary convolution.
  
      Returns:
          a function for performing binary convolution
      """
  
      kernel_shape = (num_filters, channels) + filter_shape
      W = C.Parameter(shape=kernel_shape, init=init, name="filter")
  
      def convolution(operand):
          bcv_operand_p = C.placeholder(
              operand.shape, operand.dynamic_axes, name="operand"
          )
  
          bcv = C.convolution(
              CustomMultibit(W, 1),
              CustomMultibit(bcv_operand_p, 1),
              auto_padding=[False, pad, pad],
              strides=[strides],
          )
  
          return C.as_block(bcv, [(bcv_operand_p, operand)], name)
  
      return convolution
  
imports: import cntk
  from cntk.contrib.netopt.custom_convolution_ops import *
label_desc: Use image node_id 4 for calling a global function with example usage: binary_convolution(filter_shape, num_filters, channels, init, pad, strides, name) and returns: convolution, C