code: def _darknet_yolo(inputs, params, attrs, prefix):
      """Process the yolo operation."""
      num = attrs.get("n", 1)
      classes = attrs.get("classes", 1)
      input_shape = attrs.get("shape")
      split_size = classes + 5
      intermediate_shape = (
          input_shape[0],
          num,
          split_size,
          input_shape[2],
          input_shape[3],
      )
      data_block = get_relay_op("reshape")(
          inputs[0], newshape=intermediate_shape
      )
      split_indices = (2, 4)
      split_res = get_relay_op("split")(
          data_block, indices_or_sections=split_indices, axis=2
      )
      split_res0 = get_relay_op("sigmoid")(split_res[0])
      split_res2 = get_relay_op("sigmoid")(split_res[2])
      out = get_relay_op("concatenate")(
          (split_res0, split_res[1], split_res2), axis=2
      )
      return get_relay_op("reshape")(out, newshape=input_shape)
  
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 18 for calling a global function with example usage: _darknet_yolo(inputs, params, attrs, prefix) and returns: get_relay_op