code: def _darknet_shortcut(inputs, params, attrs, prefix):
      """Process the shortcut operation."""
      input_0 = inputs[0]
      input_1 = inputs[1]
  
      input_0_channel = int(attrs["out_channel"])
      input_1_channel = int(attrs["add_out_channel"])
      input_0_size = int(attrs["out_size"])
      input_1_size = int(attrs["add_out_size"])
  
      if input_0_size > input_1_size:
          scale = int(input_0_size / input_1_size)
          input_1 = get_relay_op("upsampling")(
              input_1, scale_h=scale, scale_w=scale
          )
  
      elif input_0_size < input_1_size:
          stride = int(input_1_size / input_0_size)
          input_1 = get_relay_op("avg_pool2d")(
              input_1,
              pool_size=(1, 1),
              strides=(stride, stride),
              padding=(0, 0),
          )
  
      if input_0_channel != input_1_channel:
          pad_channel = input_0_channel - input_1_channel
          input_1 = get_relay_op("pad")(
              input_1,
              pad_width=((0, 0), (0, pad_channel), (0, 0), (0, 0)),
              pad_value=0.0,
          )
      sym = input_0 + input_1
      if "activation" in attrs:
          new_attrs = {}
          new_attrs["activation"] = attrs["activation"]
          sym = _darknet_activations(sym, None, new_attrs)
      return sym
  
imports: from enum import Enum
  import numpy
  import tvm
  from tvm.ir import IRModule
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from .common import get_relay_op, new_var
label_desc: Use image node_id 8 for calling a global function with example usage: _darknet_shortcut(inputs, params, attrs, prefix) and returns: sym