code: def test_incorrect_weights(self):
      N, P1, P2 = 16, 64, 128
      device = get_random_cuda_device()
      p1 = torch.rand(
          (N, P1, 3),
          dtype=torch.float32,
          device=device,
          requires_grad=True,
      )
      p2 = torch.rand(
          (N, P2, 3),
          dtype=torch.float32,
          device=device,
          requires_grad=True,
      )
  
      weights = torch.zeros((N,), dtype=torch.float32, device=device)
      loss, loss_norm = chamfer_distance(
          p1, p2, weights=weights, batch_reduction="mean"
      )
      self.assertClose(loss.cpu(), torch.zeros(()))
      self.assertTrue(loss.requires_grad)
      self.assertClose(loss_norm.cpu(), torch.zeros(()))
      self.assertTrue(loss_norm.requires_grad)
  
      loss, loss_norm = chamfer_distance(
          p1, p2, weights=weights, batch_reduction=None
      )
      self.assertClose(loss.cpu(), torch.zeros((N, N)))
      self.assertTrue(loss.requires_grad)
      self.assertClose(loss_norm.cpu(), torch.zeros((N, N)))
      self.assertTrue(loss_norm.requires_grad)
  
      weights = (
          torch.ones((N,), dtype=torch.float32, device=device) * -1
      )
      with self.assertRaises(ValueError):
          loss, loss_norm = chamfer_distance(p1, p2, weights=weights)
  
      weights = torch.zeros(
          (N - 1,), dtype=torch.float32, device=device
      )
      with self.assertRaises(ValueError):
          loss, loss_norm = chamfer_distance(p1, p2, weights=weights)
  
imports: import unittest
  from collections import namedtuple
  import numpy
  import torch
  import torch.nn.functional
  from pytorch3d.loss import chamfer_distance
  from pytorch3d.structures.pointclouds import Pointclouds
  from .common_testing import get_random_cuda_device, TestCaseMixin
label_desc: Use image node_id 19 for calling the TestChamfer obj's underlying member method code with example usage: obj.test_incorrect_weights() without return types