code: def test_conv(self):
      for conv_op in [conv.conv2d, conv2d]:
          for border_mode in ["valid", "full"]:
              image_shape = (2, 2, 4, 5)
              filter_shape = (2, 2, 2, 3)
              image_dim = len(image_shape)
              filter_dim = len(filter_shape)
              input = tensor.TensorType(
                  theano.config.floatX, [False] * image_dim
              )(name="input")
              filters = tensor.TensorType(
                  theano.config.floatX, [False] * filter_dim
              )(name="filter")
              ev_input = tensor.TensorType(
                  theano.config.floatX, [False] * image_dim
              )(name="ev_input")
              ev_filters = tensor.TensorType(
                  theano.config.floatX, [False] * filter_dim
              )(name="ev_filters")
  
              def sym_conv2d(input, filters):
                  return conv_op(
                      input, filters, border_mode=border_mode
                  )
  
              output = sym_conv2d(input, filters).flatten()
              yv = tensor.Rop(
                  output, [input, filters], [ev_input, ev_filters]
              )
              mode = None
              if theano.config.mode == "FAST_COMPILE":
                  mode = "FAST_RUN"
              rop_f = function(
                  [input, filters, ev_input, ev_filters],
                  yv,
                  on_unused_input="ignore",
                  mode=mode,
              )
              sy, _ = theano.scan(
                  lambda i, y, x1, x2, v1, v2: (
                      tensor.grad(y[i], x1) * v1
                  ).sum()
                  + (tensor.grad(y[i], x2) * v2).sum(),
                  sequences=tensor.arange(output.shape[0]),
                  non_sequences=[
                      output,
                      input,
                      filters,
                      ev_input,
                      ev_filters,
                  ],
                  mode=mode,
              )
              scan_f = function(
                  [input, filters, ev_input, ev_filters],
                  sy,
                  on_unused_input="ignore",
                  mode=mode,
              )
              dtype = theano.config.floatX
              image_data = np.random.random(image_shape).astype(dtype)
              filter_data = np.random.random(filter_shape).astype(dtype)
              ev_image_data = np.random.random(image_shape).astype(
                  dtype
              )
              ev_filter_data = np.random.random(filter_shape).astype(
                  dtype
              )
              v1 = rop_f(
                  image_data, filter_data, ev_image_data, ev_filter_data
              )
              v2 = scan_f(
                  image_data, filter_data, ev_image_data, ev_filter_data
              )
              assert np.allclose(v1, v2), "Rop mismatch: %s %s" % (
                  v1,
                  v2,
              )
  
imports: from __future__ import absolute_import, print_function, division
  import unittest
  from theano.tests import unittest_tools
  from theano import function
  import theano
  from theano import tensor
  import itertools
  import numpy
  from theano.gof import Op, Apply
  from theano.gradient import grad_undefined
  from theano.tests.unittest_tools import SkipTest
  from theano.tensor.signal.pool import Pool
  from theano.tensor.nnet import conv, conv2d
label_desc: Use image node_id 14 for calling the test_RopLop obj's underlying member method code with example usage: obj.test_conv() and returns: conv_op