code: def test_smoke(self, device, dtype):
      def _create_tensor_box():
          # Sample two points of the rectangle
          points = torch.rand(1, 4, device=device, dtype=dtype)
  
          # Fill according missing points
          tensor_boxes = torch.zeros(
              1, 4, 2, device=device, dtype=dtype
          )
          tensor_boxes[0, 0] = points[0][:2]
          tensor_boxes[0, 1, 0] = points[0][2]
          tensor_boxes[0, 1, 1] = points[0][1]
          tensor_boxes[0, 2] = points[0][2:]
          tensor_boxes[0, 3, 0] = points[0][0]
          tensor_boxes[0, 3, 1] = points[0][3]
          return tensor_boxes
  
      # Validate
      assert Boxes(_create_tensor_box())  # Validate 1 box
  
      # 2 boxes without batching (N, 4, 2) where N=2
      two_boxes = torch.cat(
          [_create_tensor_box(), _create_tensor_box()]
      )
      assert Boxes(two_boxes)
  
      # 2 boxes in batch (B, 1, 4, 2) where B=2
      batched_bbox = torch.stack(
          [_create_tensor_box(), _create_tensor_box()]
      )
      assert Boxes(batched_bbox)
  
imports: from functools import partial
  import pytest
  import torch
  from kornia.geometry.boxes import Boxes, Boxes3D
  from kornia.testing import assert_close, gradcheck, tensor_to_gradcheck_var
label_desc: Use image node_id 1 for calling the TestBoxes2D obj's underlying member method code with example usage: obj.test_smoke(device, dtype) and returns: tensor_boxes