class_docstr: A helper class for handling relay functions from darknet model.
code: def _make_outlist(self, sym, op_name, layer, layer_num):
      layer_type = LAYERTYPE(layer.type)
      if layer_type == LAYERTYPE.REGION:
          # Add attributes
          k = _get_params_name(op_name, "attr")
          dshape = self._tvmparams[k].shape
          dtype = self._tvmparams[k].dtype
          self._outs.insert(0, new_var(k, shape=dshape, dtype=dtype))
  
          # Add bias
          k = _get_params_name(op_name, "bias")
          dshape = self._tvmparams[k].shape
          dtype = self._tvmparams[k].dtype
          self._outs.insert(0, new_var(k, shape=dshape, dtype=dtype))
          if layer_num != self._net.n - 1:
              self._outs.insert(0, sym)
  
      elif layer_type == LAYERTYPE.YOLO:
          # Add attributes
          k = _get_params_name(op_name, "attr")
          dshape = self._tvmparams[k].shape
          dtype = self._tvmparams[k].dtype
          self._outs.insert(0, new_var(k, shape=dshape, dtype=dtype))
  
          # Add bias
          k = _get_params_name(op_name, "bias")
          dshape = self._tvmparams[k].shape
          dtype = self._tvmparams[k].dtype
          self._outs.insert(0, new_var(k, shape=dshape, dtype=dtype))
  
          # Add mask
          k = _get_params_name(op_name, "mask")
          dshape = self._tvmparams[k].shape
          dtype = self._tvmparams[k].dtype
          self._outs.insert(0, new_var(k, shape=dshape, dtype=dtype))
  
          if layer_num != self._net.n - 1:
              self._outs.insert(0, 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 16 for calling the GraphProto obj's underlying member method code with example usage: obj._make_outlist(sym, op_name, layer, layer_num) without return types