code: def test_dataset_timeseries():
      gdf1 = gd.datasets.timeseries(
          dtypes={"x": int, "y": float},
          freq="120s",
          nulls_frequency=0.3,
          seed=1,
      )
      gdf2 = gd.datasets.timeseries(
          dtypes={"x": int, "y": float},
          freq="120s",
          nulls_frequency=0.3,
          seed=1,
      )
  
      assert_eq(gdf1, gdf2)
  
      assert gdf1["x"].head().dtype == int
      assert gdf1["y"].head().dtype == float
      assert gdf1.index.name == "timestamp"
  
      gdf = gd.datasets.timeseries(
          "2000",
          "2010",
          freq="2H",
          dtypes={"value": float, "name": "category", "id": int},
          nulls_frequency=0.7,
          seed=1,
      )
  
      assert gdf["value"].head().dtype == float
      assert gdf["id"].head().dtype == int
      assert gdf["name"].head().dtype == "category"
  
      gdf = gd.datasets.randomdata()
      assert gdf["id"].head().dtype == int
      assert gdf["x"].head().dtype == float
      assert gdf["y"].head().dtype == float
      assert len(gdf) == 10
  
      gdf = gd.datasets.randomdata(
          nrows=20, dtypes={"id": int, "a": int, "b": float}
      )
      assert gdf["id"].head().dtype == int
      assert gdf["a"].head().dtype == int
      assert gdf["b"].head().dtype == float
      assert len(gdf) == 20
  
imports: import numpy
  import cudf
  from cudf.testing._utils import assert_eq
label_desc: Use image node_id 1 for calling a global function with example usage: test_dataset_timeseries() without return types