code: def test_list_to_1d_numpy(collection, dtype):
      collection2y = {
          "1d_np": np.random.rand(10),
          "2d_np": np.random.rand(10, 1),
          "pd_float": np.random.rand(10),
          "pd_str": ["a", "b"],
          "1d_list": [1] * 10,
          "2d_list": [[1], [2]],
      }
      y = collection2y[collection]
      if collection.startswith("pd"):
          if not PANDAS_INSTALLED:
              pytest.skip("pandas is not installed")
          else:
              y = pd_Series(y)
      if isinstance(y, np.ndarray) and len(y.shape) == 2:
          with pytest.warns(UserWarning, match="column-vector"):
              lgb.basic._list_to_1d_numpy(
                  y, dtype=np.float32, name="list"
              )
          return
      elif isinstance(y, list) and isinstance(y[0], list):
          with pytest.raises(TypeError):
              lgb.basic._list_to_1d_numpy(
                  y, dtype=np.float32, name="list"
              )
          return
      elif isinstance(y, pd_Series) and y.dtype == object:
          with pytest.raises(ValueError):
              lgb.basic._list_to_1d_numpy(
                  y, dtype=np.float32, name="list"
              )
          return
      result = lgb.basic._list_to_1d_numpy(y, dtype=dtype, name="list")
      assert result.size == 10
      assert result.dtype == dtype
  
imports: import filecmp
  import numbers
  import re
  from copy import deepcopy
  from os import getenv
  from pathlib import Path
  import numpy
  import pytest
  from scipy import sparse
  from sklearn.datasets import dump_svmlight_file, load_svmlight_file
  from sklearn.model_selection import train_test_split
  import lightgbm
  from lightgbm.compat import PANDAS_INSTALLED, pd_DataFrame, pd_Series
  from .utils import dummy_obj, load_breast_cancer, mse_obj, np_assert_array_equal
label_desc: Use image node_id 22 for calling a global function with example usage: test_list_to_1d_numpy(collection, dtype) without return types