code: def test_vision_transformer_head_normalize_inputs(self):
      batch_size = 2
      in_plane = 3
      head = VisionTransformerHead(
          "default_head",
          num_classes=None,
          in_plane=in_plane,
          normalize_inputs="l2",
      )
      input = torch.rand([batch_size, in_plane])
      output = head(input)
      self.assertEqual(output.shape, torch.Size([batch_size, in_plane]))
      for i in range(batch_size):
          output_i = output[i]
          self.assertAlmostEqual(output_i.norm().item(), 1, places=3)
  
imports: import unittest
  import torch
  from classy_vision.heads.vision_transformer_head import VisionTransformerHead
label_desc: Use image node_id 2 for calling the TestVisionTransformerHead obj's underlying member method code with example usage: obj.test_vision_transformer_head_normalize_inputs() without return types