code: def test_custom_input_data():
      input_data = [
          ((torch.randn(2, 3, 10, 10),), torch.randn(2, 1)),
          ((torch.randn(2, 3, 10, 10),), torch.randn(2, 1)),
          ((torch.randn(2, 3, 10, 10),), torch.randn(2, 1)),
          ((torch.randn(2, 3, 10, 10),), torch.randn(2, 1)),
      ]
  
      data_manager = DataManager(input_data)
  
      assert len(data_manager) == 4
      assert len(data_manager[0]) == 2
      assert len(data_manager[0][0]) == 1
      assert data_manager[0][0][0].shape == (2, 3, 10, 10)
      assert data_manager[0][1].shape == (2, 1)
  
file_path: nebuly/optimization/nebullvm/nebullvm/tools/tests/test_data.py
imports: import tensorflow
  import torch
  from nebullvm.tools.data import DataManager
label_desc: Use image node_id 1 for calling a global function with example usage: test_custom_input_data() without return types