code: def test_tensor_data_class_to_cuda(self):
      # Test if TensorDataClass.to(...) can move to designated device
      batch_size = 4
      dim = 5
      float_features = torch.randn(batch_size, dim)
      keys = ["Key0", "Key1", "Key2"]
      values = torch.arange(10).float()
      lengths = torch.tensor([2, 0, 1, 1, 1, 1, 3, 0, 0, 1, 0, 0])
      id_list_features = KeyedJaggedTensor(
          keys=keys, values=values, lengths=lengths
      )
      id_list_features_raw = {
          "key0": (
              torch.randn(batch_size, dim),
              torch.randn(batch_size, dim),
          )
      }
      data = rlt.FeatureData(
          float_features=float_features,
          id_list_features=id_list_features,
          id_list_features_raw=id_list_features_raw,
      )
      data_cuda = data.to(torch.device("cuda"))
      assert data_cuda.float_features.device.type == "cuda"
      assert data_cuda.id_list_features.values().device.type == "cuda"
      assert data_cuda.id_list_features.lengths().device.type == "cuda"
      assert (
          data_cuda.id_list_features_raw["key0"][0].device.type
          == "cuda"
      )
      assert (
          data_cuda.id_list_features_raw["key0"][1].device.type
          == "cuda"
      )
  
imports: import unittest
  import reagent.core.types
  import torch
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 1 for calling the TestTypes obj's underlying member method code with example usage: obj.test_tensor_data_class_to_cuda() without return types