code: def test_to_dask(ray_start_regular_shared, ds_format):
      from ray.util.dask import ray_dask_get
  
      df1 = pd.DataFrame({"one": [1, 2, 3], "two": ["a", "b", "c"]})
      df2 = pd.DataFrame({"one": [4, 5, 6], "two": ["e", "f", "g"]})
      df = pd.concat([df1, df2])
      ds = ray.data.from_pandas([df1, df2])
      if ds_format == "arrow":
          ds = ds.map_batches(
              lambda df: df, batch_format="pyarrow", batch_size=None
          )
      ddf = ds.to_dask()
      meta = ddf._meta
      # Check metadata.
      assert isinstance(meta, pd.DataFrame)
      assert meta.empty
      assert list(meta.columns) == ["one", "two"]
      assert list(meta.dtypes) == [np.int64, object]
      # Explicit Dask-on-Ray
      assert df.equals(ddf.compute(scheduler=ray_dask_get))
      # Implicit Dask-on-Ray.
      assert df.equals(ddf.compute())
  
      # Explicit metadata.
      df1["two"] = df1["two"].astype(pd.StringDtype())
      df2["two"] = df2["two"].astype(pd.StringDtype())
      df = pd.concat([df1, df2])
      ds = ray.data.from_pandas([df1, df2])
      if ds_format == "arrow":
          ds = ds.map_batches(
              lambda df: df, batch_format="pyarrow", batch_size=None
          )
      ddf = ds.to_dask(
          meta=pd.DataFrame(
              {
                  "one": pd.Series(dtype=np.int16),
                  "two": pd.Series(dtype=pd.StringDtype()),
              }
          ),
      )
      meta = ddf._meta
      # Check metadata.
      assert isinstance(meta, pd.DataFrame)
      assert meta.empty
      assert list(meta.columns) == ["one", "two"]
      assert list(meta.dtypes) == [np.int16, pd.StringDtype()]
      # Explicit Dask-on-Ray
      assert df.equals(ddf.compute(scheduler=ray_dask_get))
      # Implicit Dask-on-Ray.
      assert df.equals(ddf.compute())
  
      # Test case with blocks which have different schema, where we must
      # skip the metadata check in order to avoid a Dask metadata mismatch error.
      df1 = pd.DataFrame({"one": [1, 2, 3], "two": ["a", "b", "c"]})
      df2 = pd.DataFrame({"three": [4, 5, 6], "four": ["e", "f", "g"]})
      df = pd.concat([df1, df2])
      ds = ray.data.from_pandas([df1, df2])
      if ds_format == "arrow":
          ds = ds.map_batches(
              lambda df: df, batch_format="pyarrow", batch_size=None
          )
      ddf = ds.to_dask(verify_meta=False)
  
      # Explicit Dask-on-Ray
      assert df.equals(ddf.compute(scheduler=ray_dask_get))
      # Implicit Dask-on-Ray.
      assert df.equals(ddf.compute())
  
file_path: ray/python/ray/data/tests/test_ecosystem.py
imports: import sys
  import numpy
  import pandas
  import pyarrow
  import pytest
  import ray
  from ray.data.extensions.tensor_extension import ArrowTensorArray, ArrowTensorType, TensorArray, TensorDtype
  from ray.data.tests.conftest import *
  from ray.tests.conftest import *
label_desc: Use image node_id 2 for calling a global function with example usage: test_to_dask(ray_start_regular_shared, ds_format) without return types