code: def test_variations_input_dimension(self):
      device = "cuda" if torch.cuda.is_available() else "cpu"
      for model_name in self.resnet_variants:
          for input_width, input_height in zip([32, 64], [64, 64]):
              resnet = ResNetGenerator(model_name)
              model = MoCo(get_backbone(resnet, num_ftrs=32)).to(device)
  
              input_tensor = torch.rand(
                  (self.batch_size, 3, input_height, input_width)
              )
              with torch.no_grad():
                  out = model(input_tensor.to(device))
  
              self.assertIsNotNone(model)
              self.assertIsNotNone(out)
  
file_path: lightly/tests/models/test_ModelsMoCo.py
imports: import unittest
  import torch
  import torch.nn
  import torchvision
  import lightly
  from lightly.models import MoCo, ResNetGenerator
label_desc: Use image node_id 5 for calling the TestModelsMoCo obj's underlying member method code with example usage: obj.test_variations_input_dimension() without return types