code: def make_timeseries(
      start="2000-01-01",
      end="2000-12-31",
      dtypes=None,
      freq="10s",
      partition_freq=f"1{_ME}",
      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.dataframe as dd
      >>> df = dd.demo.make_timeseries('2000', '2010',
      ...                              {'value': float, 'name': str, 'id': int},
      ...                              freq='2H', partition_freq='1D', seed=1)
      >>> df.head()  # doctest: +SKIP
                             id      name     value
      2000-01-01 00:00:00   969     Jerry -0.309014
      2000-01-01 02:00:00  1010       Ray -0.760675
      2000-01-01 04:00:00  1016  Patricia -0.063261
      2000-01-01 06:00:00   960   Charlie  0.788245
      2000-01-01 08:00:00  1031     Kevin  0.466002
      """
      if dtypes is None:
          dtypes = {"name": str, "id": int, "x": float, "y": float}
  
      divisions = list(
          pd.date_range(start=start, end=end, freq=partition_freq)
      )
      npartitions = len(divisions) - 1
      if seed is None:
          # Get random integer seed for each partition. We can
          # call `random_state_data` in `MakeDataframePart`
          state_data = np.random.randint(2e9, size=npartitions)
      else:
          state_data = random_state_data(npartitions, seed)
  
      # Build parts
      parts = []
      for i in range(len(divisions) - 1):
          parts.append((divisions[i : i + 2], state_data[i]))
  
      kwargs["freq"] = freq
      index_dtype = "datetime64[ns]"
      meta_start, meta_end = list(
          pd.date_range(start="2000", freq=freq, periods=2)
      )
  
      # Construct the output collection with from_map
      return from_map(
          MakeDataframePart(index_dtype, dtypes, kwargs),
          parts,
          meta=make_dataframe_part(
              index_dtype,
              meta_start,
              meta_end,
              dtypes,
              list(dtypes.keys()),
              state_data[0],
              kwargs,
          ),
          divisions=divisions,
          label="make-timeseries",
          token=tokenize(
              start, end, dtypes, freq, partition_freq, state_data
          ),
          enforce_metadata=False,
      )
  
imports: from __future__ import annotations
  import re
  import string
  from dataclasses import asdict, dataclass, field
  from typing import Any, Callable, cast
  import numpy
  import pandas
  from dask.dataframe._compat import PANDAS_GE_220
  from dask.dataframe._pyarrow import is_object_string_dtype
  from dask.dataframe.core import tokenize
  from dask.dataframe.io.io import from_map
  from dask.dataframe.io.utils import DataFrameIOFunction
  from dask.utils import random_state_data
label_desc: Use image node_id 9 for calling a global function with example usage: make_timeseries(start, end, dtypes, freq, partition_freq, seed) and returns: from_map