code: def test_dino_projection_head(self, device="cpu", seed=0):
      input_dim, hidden_dim, output_dim = self.n_features[0]
      for bottleneck_dim in [8, 16, 32]:
          for batch_norm in [False, True]:
              torch.manual_seed(seed)
              head = DINOProjectionHead(
                  input_dim=input_dim,
                  hidden_dim=hidden_dim,
                  output_dim=output_dim,
                  bottleneck_dim=bottleneck_dim,
                  batch_norm=batch_norm,
              )
              head = head.eval()
              head = head.to(device)
              for batch_size in [1, 2]:
                  msg = (
                      f"bottleneck_dim={bottleneck_dim}, "
                      f"batch_norm={batch_norm}"
                  )
                  with self.subTest(msg=msg):
                      x = torch.torch.rand((batch_size, input_dim)).to(
                          device
                      )
                      with torch.no_grad():
                          y = head(x)
                      self.assertEqual(y.shape[0], batch_size)
                      self.assertEqual(y.shape[1], output_dim)
  
file_path: lightly/tests/models/test_ProjectionHeads.py
imports: import unittest
  import torch
  import lightly
  from lightly.models.modules.heads import BarlowTwinsProjectionHead, BYOLPredictionHead, BYOLProjectionHead, DINOProjectionHead, MoCoProjectionHead, MSNProjectionHead, NNCLRPredictionHead, NNCLRProjectionHead, SimCLRProjectionHead, SimSiamPredictionHead, SimSiamProjectionHead, SwaVProjectionHead, SwaVPrototypes, TiCoProjectionHead, VicRegLLocalProjectionHead
label_desc: Use image node_id 8 for calling the TestProjectionHeads obj's underlying member method code with example usage: obj.test_dino_projection_head(device, seed) without return types