code: def test_date32_pre_transform_dataset():
      # Test to make sure that date32 columns are interpreted in the local timezone
      dates_df = pd.DataFrame(
          {
              "date_col": [
                  date(2022, 1, 1),
                  date(2022, 1, 2),
                  date(2022, 1, 3),
              ],
          }
      )
      spec = date_column_spec()
  
      (output_ds,), warnings = vf.runtime.pre_transform_datasets(
          spec,
          ["data_0"],
          "America/New_York",
          default_input_tz="UTC",
          inline_datasets=dict(dates=dates_df),
      )
  
      # Timestamps are in the local timezone, so they should be midnight local time
      assert list(output_ds.date_col) == [
          pd.Timestamp(
              "2022-01-01 00:00:00-0500", tz="America/New_York"
          ),
          pd.Timestamp(
              "2022-01-02 00:00:00-0500", tz="America/New_York"
          ),
          pd.Timestamp(
              "2022-01-03 00:00:00-0500", tz="America/New_York"
          ),
      ]
  
imports: import pandas
  import pytest
  from pandas import Timestamp, NaT
  import pyarrow
  import vegafusion
  import json
  import polars
  from datetime import date
  import decimal
  import base64
label_desc: Use image node_id 18 for calling a global function with example usage: test_date32_pre_transform_dataset() without return types