code: def test_instance_loss(self):
      torch.manual_seed(3029)
      for dtype in TEST_DTYPES:
          for gamma in range(1, 256, 32):
              loss_fn = InstanceLoss(gamma=gamma)
              original_loss_fn = OriginalInstanceLoss(gamma=gamma)
              embeddings = torch.randn(
                  32,
                  128,
                  device=TEST_DEVICE,
                  dtype=dtype,
                  requires_grad=True,
              )
              labels = torch.randint(
                  0, 10, size=(32,), device=TEST_DEVICE
              )
              loss = loss_fn(embeddings, labels)
              loss.backward()
  
              original_loss = original_loss_fn(embeddings, labels)
              rtol = 1e-3 if dtype == torch.float16 else 1e-6
              self.assertTrue(
                  np.isclose(
                      loss.item(), original_loss.item(), rtol=rtol
                  )
              )
  
file_path: pytorch-metric-learning/tests/losses/test_instance_loss.py
imports: import unittest
  import numpy
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from pytorch_metric_learning.losses import InstanceLoss
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 1 for calling the TestInstanceLoss obj's underlying member method code with example usage: obj.test_instance_loss() without return types