class_docstr: Test the VISSL SwinT implementation against the official implementation of:
  https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
  
  As of today, April 2022, the VISSL implementation is more flexible and allows
  input multiple resolutions without requiring up-sampling / down-sampling.
code: def test_vissl_implementation_support_multiple_resolutions(
      self,
  ) -> None:
      with torch.no_grad():
          config = self.vissl_swin_transformer_config()
          model = build_model(
              config["MODEL"], config["OPTIMIZER"]
          ).cuda()
  
          x1 = torch.randn(size=(2, 3, 224, 224)).cuda()
          y1 = model.trunk(x1)[0]
          self.assertEqual(y1.shape, torch.Size([2, 768]))
  
          x2 = torch.randn(size=(2, 3, 96, 96)).cuda()
          y2 = model.trunk(x2)[0]
          self.assertEqual(y2.shape, torch.Size([2, 768]))
  
          x3 = torch.randn(size=(2, 3, 95, 95)).cuda()
          y3 = model.trunk(x3)[0]
          self.assertEqual(y3.shape, torch.Size([2, 768]))
  
file_path: vissl/tests/test_swin_t.py
imports: import unittest
  import torch
  from vissl.models import build_model
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.test_utils import gpu_test
label_desc: Use image node_id 5 for calling the TestSwinT obj's underlying member method code with example usage: obj.test_vissl_implementation_support_multiple_resolutions() without return types