code: def test_in_batch_hinge(self):
      in_batch_hinge_loss = losses.InBatchHinge(0.2, True)
  
      sample_list_input = dict()
      predicted = dict()
  
      # Test when the image and text have the same embeddings
      predicted["targets"] = torch.Tensor(
          [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
      )
      predicted["scores"] = predicted["targets"]
      self.assertEqual(
          in_batch_hinge_loss(sample_list_input, predicted).item(), 0.0
      )
  
      # Test random initialized
      torch.manual_seed(1234)
      predicted["targets"] = torch.rand((5, 10))
      predicted["scores"] = torch.rand((5, 10))
  
      self.assertAlmostEqual(
          in_batch_hinge_loss(sample_list_input, predicted).item(),
          6.5529985,
          4,
      )
  
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 6 for calling the TestModuleLosses obj's underlying member method code with example usage: obj.test_in_batch_hinge() without return types