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 build_eval_model(config, seed=0):
      torch.manual_seed(0)
      torch.cuda.manual_seed(0)
      model = build_model(config["MODEL"], config["OPTIMIZER"]).cuda()
      model.eval()
      return model
  
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 3 for calling the TestSwinT obj's underlying member method code with example usage: obj.build_eval_model(config, seed) and returns: model