code: def timeseries(
      start="2000-01-01",
      end="2000-01-31",
      freq="1s",
      partition_freq="1d",
      dtypes=None,
      seed=None,
      **kwargs,
  ):
      """Create timeseries dataframe with random data
  
      Parameters
      ----------
      start : datetime (or datetime-like string)
          Start of time series
      end : datetime (or datetime-like string)
          End of time series
      dtypes : dict (optional)
          Mapping of column names to types.
          Valid types include {float, int, str, 'category'}
      freq : string
          String like '2s' or '1H' or '12W' for the time series frequency
      partition_freq : string
          String like '1M' or '2Y' to divide the dataframe into partitions
      seed : int (optional)
          Randomstate seed
      kwargs:
          Keywords to pass down to individual column creation functions.
          Keywords should be prefixed by the column name and then an underscore.
  
      Examples
      --------
      >>> import dask
      >>> df = dask.datasets.timeseries()
      >>> df.head()  # doctest: +SKIP
                timestamp    id     name         x         y
      2000-01-01 00:00:00   967    Jerry -0.031348 -0.040633
      2000-01-01 00:00:01  1066  Michael -0.262136  0.307107
      2000-01-01 00:00:02   988    Wendy -0.526331  0.128641
      2000-01-01 00:00:03  1016   Yvonne  0.620456  0.767270
      2000-01-01 00:00:04   998   Ursula  0.684902 -0.463278
      >>> df = dask.datasets.timeseries(
      ...     '2000', '2010',
      ...     freq='2H', partition_freq='1D', seed=1,  # data frequency
      ...     dtypes={'value': float, 'name': str, 'id': int},  # data types
      ...     id_lam=1000  # control number of items in id column
      ... )
      """
      from dask.dataframe.io.demo import make_timeseries
  
      if dtypes is None:
          dtypes = {"name": str, "id": int, "x": float, "y": float}
  
      return make_timeseries(
          start=start,
          end=end,
          freq=freq,
          partition_freq=partition_freq,
          seed=seed,
          dtypes=dtypes,
          **kwargs,
      )
  
imports: from __future__ import annotations
  import random
  from packaging.version import Version
  from dask.utils import import_required
label_desc: Use image node_id 1 for calling a global function with example usage: timeseries(start, end, freq, partition_freq, dtypes, seed) and returns: make_timeseries