code: def verify_darknet_frontend(net, build_dtype="float32"):
      """Test network with given input image on both darknet and tvm"""
  
      def get_darknet_output(net, img):
          _lib().network_predict_image(net, img)
          out = []
          for i in range(net.n):
              layer = net.layers[i]
              if layer.type == LAYERTYPE.REGION:
                  attributes = np.array(
                      [
                          layer.n,
                          layer.out_c,
                          layer.out_h,
                          layer.out_w,
                          layer.classes,
                          layer.coords,
                          layer.background,
                      ],
                      dtype=np.int32,
                  )
                  out.insert(0, attributes)
                  out.insert(
                      0,
                      _read_memory_buffer((layer.n * 2,), layer.biases),
                  )
                  layer_outshape = (
                      layer.batch,
                      layer.out_c,
                      layer.out_h,
                      layer.out_w,
                  )
                  out.insert(
                      0,
                      _read_memory_buffer(layer_outshape, layer.output),
                  )
              elif layer.type == LAYERTYPE.YOLO:
                  attributes = np.array(
                      [
                          layer.n,
                          layer.out_c,
                          layer.out_h,
                          layer.out_w,
                          layer.classes,
                          layer.total,
                      ],
                      dtype=np.int32,
                  )
                  out.insert(0, attributes)
                  out.insert(
                      0,
                      _read_memory_buffer(
                          (layer.total * 2,), layer.biases
                      ),
                  )
                  out.insert(
                      0,
                      _read_memory_buffer(
                          (layer.n,), layer.mask, dtype="int32"
                      ),
                  )
                  layer_outshape = (
                      layer.batch,
                      layer.out_c,
                      layer.out_h,
                      layer.out_w,
                  )
                  out.insert(
                      0,
                      _read_memory_buffer(layer_outshape, layer.output),
                  )
              elif i == net.n - 1:
                  if layer.type == LAYERTYPE.CONNECTED:
                      darknet_outshape = (layer.batch, layer.out_c)
                  elif layer.type in [LAYERTYPE.SOFTMAX]:
                      darknet_outshape = (layer.batch, layer.outputs)
                  else:
                      darknet_outshape = (
                          layer.batch,
                          layer.out_c,
                          layer.out_h,
                          layer.out_w,
                      )
                  out.insert(
                      0,
                      _read_memory_buffer(
                          darknet_outshape, layer.output
                      ),
                  )
          return out
  
      dtype = "float32"
  
      img = _lib().letterbox_image(
          _lib().load_image_color(
              _darknet_test_image_path().encode("utf-8"), 0, 0
          ),
          net.w,
          net.h,
      )
      darknet_output = get_darknet_output(net, img)
      batch_size = 1
      data = np.empty([batch_size, img.c, img.h, img.w], dtype)
      i = 0
      for c in range(img.c):
          for h in range(img.h):
              for k in range(img.w):
                  data[0][c][h][k] = img.data[i]
                  i = i + 1
  
      tvm_out = _get_tvm_output(net, data, build_dtype)
      for tvm_outs, darknet_out in zip(tvm_out, darknet_output):
          tvm.testing.assert_allclose(
              darknet_out, tvm_outs, rtol=1e-3, atol=1e-3
          )
  
imports: from cffi import FFI
  import numpy
  import tvm
  from tvm.contrib import graph_executor
  from tvm.contrib.download import download_testdata
  from tvm.relay.testing.darknet import LAYERTYPE
  from tvm.relay.testing.darknet import __darknetffi__
  from tvm.relay.frontend.darknet import ACTIVATION
  from tvm import relay
label_desc: Use image node_id 7 for calling a global function with example usage: verify_darknet_frontend(net, build_dtype) and returns: out