code: def test_ms_loss(self):
      ms_loss = losses.MSLoss(
          alpha=50,
          beta=2,
          margin=0.5,
          hard_mining=True,
          is_multilabel=False,
      )
  
      torch.manual_seed(1234)
  
      label_tensor = torch.Tensor([0, 0, 0, 0, 0])
      fused_tensor = torch.randn(5, 768)
      sample_list = {"targets": label_tensor}
      model_output = {"fused_embedding": fused_tensor}
      loss_result = ms_loss(sample_list, model_output)
      self.assertEqual(loss_result, 0.0)
  
      label_tensor = torch.Tensor([1, 1, 1, 1, 1])
      loss_result = ms_loss(sample_list, model_output)
      self.assertEqual(loss_result, 0.0)
  
      label_tensor = torch.Tensor([1, 1, 1, 1, 1])
      loss_result = ms_loss(sample_list, model_output)
      self.assertEqual(loss_result, 0.0)
  
imports: import collections
  import unittest
  from unittest.mock import MagicMock
  import mmf.modules.losses
  import torch
  import torch.nn
  import torch.nn.functional
  from mmf.common.registry import registry
  from mmf.common.sample import SampleList
label_desc: Use image node_id 11 for calling the TestModuleLosses obj's underlying member method code with example usage: obj.test_ms_loss() without return types