code: def test_tensor_column_no_memory_leak(self):
      # Test that converting a Pandas DataFrame containing an object-dtyped tensor
      # column (e.g. post-casting from extension type) doesn't leak memory. Casting
      # these tensors directly with torch.as_tensor() currently leaks memory; see
      # https://github.com/ray-project/ray/issues/30629#issuecomment-1330954556
      def code():
          col = np.empty(1000, dtype=object)
          col[:] = [np.ones((100, 100)) for _ in range(1000)]
          df = pd.DataFrame({"a": col})
          convert_pandas_to_torch_tensor(
              df, columns=[["a"]], column_dtypes=[torch.int]
          )
  
      suspicious_stats = _test_some_code_for_memory_leaks(
          desc="Testing convert_pandas_to_torch_tensor for memory leaks.",
          init=None,
          code=code,
          repeats=10,
      )
      assert not suspicious_stats
  
file_path: ray/python/ray/train/tests/test_torch_utils.py
imports: import numpy
  import pandas
  import pytest
  import torch
  from ray.air._internal.torch_utils import contains_tensor, convert_pandas_to_torch_tensor, load_torch_model
  from ray.util.debug import _test_some_code_for_memory_leaks
label_desc: Use image node_id 8 for calling the TestConvertPandasToTorch obj's underlying member method code with example usage: obj.test_tensor_column_no_memory_leak() without return types