text: import sys
  import torch
  
  _onnx_opset_version = 11
  
  
  def _register_custom_op():
      from torch.onnx.symbolic_helper import parse_args, scalar_type_to_onnx, scalar_type_to_pytorch_type, \
          cast_pytorch_to_onnx
      from torch.onnx.symbolic_opset9 import select, unsqueeze, squeeze, _cast_Long, reshape
  
      @parse_args('v', 'v', 'f')
      def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
          boxes = unsqueeze(g, boxes, 0)
          scores = unsqueeze(g, unsqueeze(g, scores, 0), 0)
          max_output_per_class = g.op('Constant', value_t=torch.tensor([sys.maxsize], dtype=torch.long))
          iou_threshold = g.op('Constant', value_t=torch.tensor([iou_threshold], dtype=torch.float))
          nms_out = g.op('NonMaxSuppression', boxes, scores, max_output_per_class, iou_threshold)
          return squeeze(g, select(g, nms_out, 1, g.op('Constant', value_t=torch.tensor([2], dtype=torch.long))), 1)
  
      @parse_args('v', 'v', 'f', 'i', 'i', 'i')
      def roi_align(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio):
          batch_indices = _cast_Long(g, squeeze(g, select(g, rois, 1, g.op('Constant',
                                     value_t=torch.tensor([0], dtype=torch.long))), 1), False)
          rois = select(g, rois, 1, g.op('Constant', value_t=torch.tensor([1, 2, 3, 4], dtype=torch.long)))
          return g.op('RoiAlign', input, rois, batch_indices, spatial_scale_f=spatial_scale,
                      output_height_i=pooled_height, output_width_i=pooled_width, sampling_ratio_i=sampling_ratio)
  
      @parse_args('v', 'v', 'f', 'i', 'i')
      def roi_pool(g, input, rois, spatial_scale, pooled_height, pooled_width):
          roi_pool = g.op('MaxRoiPool', input, rois,
                          pooled_shape_i=(pooled_height, pooled_width), spatial_scale_f=spatial_scale)
          return roi_pool, None
  
      @parse_args('v', 'is')
      def new_empty_tensor_op(g, input, shape):
          dtype = input.type().scalarType()
          if dtype is None:
              dtype = 'Float'
          dtype = scalar_type_to_onnx.index(cast_pytorch_to_onnx[dtype])
          shape = g.op("Constant", value_t=torch.tensor(shape))
          return g.op("ConstantOfShape", shape,
                      value_t=torch.tensor([0], dtype=scalar_type_to_pytorch_type[dtype]))
  
      from torch.onnx import register_custom_op_symbolic
      register_custom_op_symbolic('torchvision::nms', symbolic_multi_label_nms, _onnx_opset_version)
      register_custom_op_symbolic('torchvision::roi_align', roi_align, _onnx_opset_version)
      register_custom_op_symbolic('torchvision::roi_pool', roi_pool, _onnx_opset_version)
      register_custom_op_symbolic('torchvision::_new_empty_tensor_op', new_empty_tensor_op, _onnx_opset_version)
  