We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Tests for dummy data generation failing
Looks like there are some tests failing for the dummy data generation

https://dask.github.io/dask/test_short_report.html

https://github.com/dask/dask/actions/runs/7650514559/job/20846717103

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `dask/datasets.py`
Content:
```
1 from __future__ import annotations
2 
3 import random
4 
5 from packaging.version import Version
6 
7 from dask.utils import import_required
8 
9 
10 def timeseries(
11     start="2000-01-01",
12     end="2000-01-31",
13     freq="1s",
14     partition_freq="1d",
15     dtypes=None,
16     seed=None,
17     **kwargs,
18 ):
19     """Create timeseries dataframe with random data
20 
21     Parameters
22     ----------
23     start : datetime (or datetime-like string)
24         Start of time series
25     end : datetime (or datetime-like string)
26         End of time series
27     dtypes : dict (optional)
28         Mapping of column names to types.
29         Valid types include {float, int, str, 'category'}
30     freq : string
31         String like '2s' or '1H' or '12W' for the time series frequency
32     partition_freq : string
33         String like '1M' or '2Y' to divide the dataframe into partitions
34     seed : int (optional)
35         Randomstate seed
36     kwargs:
37         Keywords to pass down to individual column creation functions.
38         Keywords should be prefixed by the column name and then an underscore.
39 
40     Examples
41     --------
42     >>> import dask
43     >>> df = dask.datasets.timeseries()
44     >>> df.head()  # doctest: +SKIP
45               timestamp    id     name         x         y
46     2000-01-01 00:00:00   967    Jerry -0.031348 -0.040633
47     2000-01-01 00:00:01  1066  Michael -0.262136  0.307107
48     2000-01-01 00:00:02   988    Wendy -0.526331  0.128641
49     2000-01-01 00:00:03  1016   Yvonne  0.620456  0.767270
50     2000-01-01 00:00:04   998   Ursula  0.684902 -0.463278
51     >>> df = dask.datasets.timeseries(
52     ...     '2000', '2010',
53     ...     freq='2h', partition_freq='1D', seed=1,  # data frequency
54     ...     dtypes={'value': float, 'name': str, 'id': int},  # data types
55     ...     id_lam=1000  # control number of items in id column
56     ... )
57     """
58     from dask.dataframe.io.demo import make_timeseries
59 
60     if dtypes is None:
61         dtypes = {"name": str, "id": int, "x": float, "y": float}
62 
63     return make_timeseries(
64         start=start,
65         end=end,
66         freq=freq,
67         partition_freq=partition_freq,
68         seed=seed,
69         dtypes=dtypes,
70         **kwargs,
71     )
72 
73 
74 def _generate_mimesis(field, schema_description, records_per_partition, seed):
75     """Generate data for a single partition of a dask bag
76 
77     See Also
78     --------
79     _make_mimesis
80     """
81     import mimesis
82     from mimesis.schema import Field, Schema
83 
84     field = Field(seed=seed, **field)
85     # `iterations=` kwarg moved from `Schema.create()` to `Schema.__init__()`
86     # starting with `mimesis=9`.
87     schema_kwargs, create_kwargs = {}, {}
88     if Version(mimesis.__version__) < Version("9.0.0"):
89         create_kwargs["iterations"] = 1
90     else:
91         schema_kwargs["iterations"] = 1
92     schema = Schema(schema=lambda: schema_description(field), **schema_kwargs)
93     return [schema.create(**create_kwargs)[0] for i in range(records_per_partition)]
94 
95 
96 def _make_mimesis(field, schema, npartitions, records_per_partition, seed=None):
97     """
98     Make a Dask Bag filled with data randomly generated by the mimesis projet
99 
100     Parameters
101     ----------
102     field: dict
103         keyword arguments to pass to ``mimesis.Field``
104     schema: Callable[Field] -> dict
105         The schema to use to generate the data
106     npartitions: int
107     records_per_partition: int
108     seed: int, None
109         Seed for random data
110 
111     Returns
112     -------
113     Dask Bag
114 
115     See Also
116     --------
117     make_people
118     """
119     import dask.bag as db
120     from dask.base import tokenize
121 
122     field = field or {}
123 
124     random_state = random.Random(seed)
125     seeds = [random_state.randint(0, 1 << 32) for _ in range(npartitions)]
126 
127     name = "mimesis-" + tokenize(
128         field, schema, npartitions, records_per_partition, seed
129     )
130     dsk = {
131         (name, i): (_generate_mimesis, field, schema, records_per_partition, seed)
132         for i, seed in enumerate(seeds)
133     }
134 
135     return db.Bag(dsk, name, npartitions)
136 
137 
138 def make_people(npartitions=10, records_per_partition=1000, seed=None, locale="en"):
139     """Make a dataset of random people
140 
141     This makes a Dask Bag with dictionary records of randomly generated people.
142     This requires the optional library ``mimesis`` to generate records.
143 
144     Parameters
145     ----------
146     npartitions : int
147         Number of partitions
148     records_per_partition : int
149         Number of records in each partition
150     seed : int, (optional)
151         Random seed
152     locale : str
153         Language locale, like 'en', 'fr', 'zh', or 'ru'
154 
155     Returns
156     -------
157     b: Dask Bag
158     """
159     import_required(
160         "mimesis",
161         "The mimesis module is required for this function.  Try:\n"
162         "  python -m pip install mimesis",
163     )
164 
165     schema = lambda field: {
166         "age": field("person.age"),
167         "name": (field("person.name"), field("person.surname")),
168         "occupation": field("person.occupation"),
169         "telephone": field("person.telephone"),
170         "address": {"address": field("address.address"), "city": field("address.city")},
171         "credit-card": {
172             "number": field("payment.credit_card_number"),
173             "expiration-date": field("payment.credit_card_expiration_date"),
174         },
175     }
176 
177     return _make_mimesis(
178         {"locale": locale}, schema, npartitions, records_per_partition, seed
179     )
180
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/dask/datasets.py b/dask/datasets.py
--- a/dask/datasets.py
+++ b/dask/datasets.py
@@ -163,7 +163,7 @@
     )
 
     schema = lambda field: {
-        "age": field("person.age"),
+        "age": field("random.randint", a=0, b=120),
         "name": (field("person.name"), field("person.surname")),
         "occupation": field("person.occupation"),
         "telephone": field("person.telephone"),

{"golden_diff": "diff --git a/dask/datasets.py b/dask/datasets.py\n--- a/dask/datasets.py\n+++ b/dask/datasets.py\n@@ -163,7 +163,7 @@\n     )\n \n     schema = lambda field: {\n-        \"age\": field(\"person.age\"),\n+        \"age\": field(\"random.randint\", a=0, b=120),\n         \"name\": (field(\"person.name\"), field(\"person.surname\")),\n         \"occupation\": field(\"person.occupation\"),\n         \"telephone\": field(\"person.telephone\"),\n", "issue": "Tests for dummy data generation failing\nLooks like there are some tests failing for the dummy data generation\r\n\r\nhttps://dask.github.io/dask/test_short_report.html\r\n\r\nhttps://github.com/dask/dask/actions/runs/7650514559/job/20846717103\n", "before_files": [{"content": "from __future__ import annotations\n\nimport random\n\nfrom packaging.version import Version\n\nfrom dask.utils import import_required\n\n\ndef timeseries(\n    start=\"2000-01-01\",\n    end=\"2000-01-31\",\n    freq=\"1s\",\n    partition_freq=\"1d\",\n    dtypes=None,\n    seed=None,\n    **kwargs,\n):\n    \"\"\"Create timeseries dataframe with random data\n\n    Parameters\n    ----------\n    start : datetime (or datetime-like string)\n        Start of time series\n    end : datetime (or datetime-like string)\n        End of time series\n    dtypes : dict (optional)\n        Mapping of column names to types.\n        Valid types include {float, int, str, 'category'}\n    freq : string\n        String like '2s' or '1H' or '12W' for the time series frequency\n    partition_freq : string\n        String like '1M' or '2Y' to divide the dataframe into partitions\n    seed : int (optional)\n        Randomstate seed\n    kwargs:\n        Keywords to pass down to individual column creation functions.\n        Keywords should be prefixed by the column name and then an underscore.\n\n    Examples\n    --------\n    >>> import dask\n    >>> df = dask.datasets.timeseries()\n    >>> df.head()  # doctest: +SKIP\n              timestamp    id     name         x         y\n    2000-01-01 00:00:00   967    Jerry -0.031348 -0.040633\n    2000-01-01 00:00:01  1066  Michael -0.262136  0.307107\n    2000-01-01 00:00:02   988    Wendy -0.526331  0.128641\n    2000-01-01 00:00:03  1016   Yvonne  0.620456  0.767270\n    2000-01-01 00:00:04   998   Ursula  0.684902 -0.463278\n    >>> df = dask.datasets.timeseries(\n    ...     '2000', '2010',\n    ...     freq='2h', partition_freq='1D', seed=1,  # data frequency\n    ...     dtypes={'value': float, 'name': str, 'id': int},  # data types\n    ...     id_lam=1000  # control number of items in id column\n    ... )\n    \"\"\"\n    from dask.dataframe.io.demo import make_timeseries\n\n    if dtypes is None:\n        dtypes = {\"name\": str, \"id\": int, \"x\": float, \"y\": float}\n\n    return make_timeseries(\n        start=start,\n        end=end,\n        freq=freq,\n        partition_freq=partition_freq,\n        seed=seed,\n        dtypes=dtypes,\n        **kwargs,\n    )\n\n\ndef _generate_mimesis(field, schema_description, records_per_partition, seed):\n    \"\"\"Generate data for a single partition of a dask bag\n\n    See Also\n    --------\n    _make_mimesis\n    \"\"\"\n    import mimesis\n    from mimesis.schema import Field, Schema\n\n    field = Field(seed=seed, **field)\n    # `iterations=` kwarg moved from `Schema.create()` to `Schema.__init__()`\n    # starting with `mimesis=9`.\n    schema_kwargs, create_kwargs = {}, {}\n    if Version(mimesis.__version__) < Version(\"9.0.0\"):\n        create_kwargs[\"iterations\"] = 1\n    else:\n        schema_kwargs[\"iterations\"] = 1\n    schema = Schema(schema=lambda: schema_description(field), **schema_kwargs)\n    return [schema.create(**create_kwargs)[0] for i in range(records_per_partition)]\n\n\ndef _make_mimesis(field, schema, npartitions, records_per_partition, seed=None):\n    \"\"\"\n    Make a Dask Bag filled with data randomly generated by the mimesis projet\n\n    Parameters\n    ----------\n    field: dict\n        keyword arguments to pass to ``mimesis.Field``\n    schema: Callable[Field] -> dict\n        The schema to use to generate the data\n    npartitions: int\n    records_per_partition: int\n    seed: int, None\n        Seed for random data\n\n    Returns\n    -------\n    Dask Bag\n\n    See Also\n    --------\n    make_people\n    \"\"\"\n    import dask.bag as db\n    from dask.base import tokenize\n\n    field = field or {}\n\n    random_state = random.Random(seed)\n    seeds = [random_state.randint(0, 1 << 32) for _ in range(npartitions)]\n\n    name = \"mimesis-\" + tokenize(\n        field, schema, npartitions, records_per_partition, seed\n    )\n    dsk = {\n        (name, i): (_generate_mimesis, field, schema, records_per_partition, seed)\n        for i, seed in enumerate(seeds)\n    }\n\n    return db.Bag(dsk, name, npartitions)\n\n\ndef make_people(npartitions=10, records_per_partition=1000, seed=None, locale=\"en\"):\n    \"\"\"Make a dataset of random people\n\n    This makes a Dask Bag with dictionary records of randomly generated people.\n    This requires the optional library ``mimesis`` to generate records.\n\n    Parameters\n    ----------\n    npartitions : int\n        Number of partitions\n    records_per_partition : int\n        Number of records in each partition\n    seed : int, (optional)\n        Random seed\n    locale : str\n        Language locale, like 'en', 'fr', 'zh', or 'ru'\n\n    Returns\n    -------\n    b: Dask Bag\n    \"\"\"\n    import_required(\n        \"mimesis\",\n        \"The mimesis module is required for this function.  Try:\\n\"\n        \"  python -m pip install mimesis\",\n    )\n\n    schema = lambda field: {\n        \"age\": field(\"person.age\"),\n        \"name\": (field(\"person.name\"), field(\"person.surname\")),\n        \"occupation\": field(\"person.occupation\"),\n        \"telephone\": field(\"person.telephone\"),\n        \"address\": {\"address\": field(\"address.address\"), \"city\": field(\"address.city\")},\n        \"credit-card\": {\n            \"number\": field(\"payment.credit_card_number\"),\n            \"expiration-date\": field(\"payment.credit_card_expiration_date\"),\n        },\n    }\n\n    return _make_mimesis(\n        {\"locale\": locale}, schema, npartitions, records_per_partition, seed\n    )\n", "path": "dask/datasets.py"}], "after_files": [{"content": "from __future__ import annotations\n\nimport random\n\nfrom packaging.version import Version\n\nfrom dask.utils import import_required\n\n\ndef timeseries(\n    start=\"2000-01-01\",\n    end=\"2000-01-31\",\n    freq=\"1s\",\n    partition_freq=\"1d\",\n    dtypes=None,\n    seed=None,\n    **kwargs,\n):\n    \"\"\"Create timeseries dataframe with random data\n\n    Parameters\n    ----------\n    start : datetime (or datetime-like string)\n        Start of time series\n    end : datetime (or datetime-like string)\n        End of time series\n    dtypes : dict (optional)\n        Mapping of column names to types.\n        Valid types include {float, int, str, 'category'}\n    freq : string\n        String like '2s' or '1H' or '12W' for the time series frequency\n    partition_freq : string\n        String like '1M' or '2Y' to divide the dataframe into partitions\n    seed : int (optional)\n        Randomstate seed\n    kwargs:\n        Keywords to pass down to individual column creation functions.\n        Keywords should be prefixed by the column name and then an underscore.\n\n    Examples\n    --------\n    >>> import dask\n    >>> df = dask.datasets.timeseries()\n    >>> df.head()  # doctest: +SKIP\n              timestamp    id     name         x         y\n    2000-01-01 00:00:00   967    Jerry -0.031348 -0.040633\n    2000-01-01 00:00:01  1066  Michael -0.262136  0.307107\n    2000-01-01 00:00:02   988    Wendy -0.526331  0.128641\n    2000-01-01 00:00:03  1016   Yvonne  0.620456  0.767270\n    2000-01-01 00:00:04   998   Ursula  0.684902 -0.463278\n    >>> df = dask.datasets.timeseries(\n    ...     '2000', '2010',\n    ...     freq='2h', partition_freq='1D', seed=1,  # data frequency\n    ...     dtypes={'value': float, 'name': str, 'id': int},  # data types\n    ...     id_lam=1000  # control number of items in id column\n    ... )\n    \"\"\"\n    from dask.dataframe.io.demo import make_timeseries\n\n    if dtypes is None:\n        dtypes = {\"name\": str, \"id\": int, \"x\": float, \"y\": float}\n\n    return make_timeseries(\n        start=start,\n        end=end,\n        freq=freq,\n        partition_freq=partition_freq,\n        seed=seed,\n        dtypes=dtypes,\n        **kwargs,\n    )\n\n\ndef _generate_mimesis(field, schema_description, records_per_partition, seed):\n    \"\"\"Generate data for a single partition of a dask bag\n\n    See Also\n    --------\n    _make_mimesis\n    \"\"\"\n    import mimesis\n    from mimesis.schema import Field, Schema\n\n    field = Field(seed=seed, **field)\n    # `iterations=` kwarg moved from `Schema.create()` to `Schema.__init__()`\n    # starting with `mimesis=9`.\n    schema_kwargs, create_kwargs = {}, {}\n    if Version(mimesis.__version__) < Version(\"9.0.0\"):\n        create_kwargs[\"iterations\"] = 1\n    else:\n        schema_kwargs[\"iterations\"] = 1\n    schema = Schema(schema=lambda: schema_description(field), **schema_kwargs)\n    return [schema.create(**create_kwargs)[0] for i in range(records_per_partition)]\n\n\ndef _make_mimesis(field, schema, npartitions, records_per_partition, seed=None):\n    \"\"\"\n    Make a Dask Bag filled with data randomly generated by the mimesis projet\n\n    Parameters\n    ----------\n    field: dict\n        keyword arguments to pass to ``mimesis.Field``\n    schema: Callable[Field] -> dict\n        The schema to use to generate the data\n    npartitions: int\n    records_per_partition: int\n    seed: int, None\n        Seed for random data\n\n    Returns\n    -------\n    Dask Bag\n\n    See Also\n    --------\n    make_people\n    \"\"\"\n    import dask.bag as db\n    from dask.base import tokenize\n\n    field = field or {}\n\n    random_state = random.Random(seed)\n    seeds = [random_state.randint(0, 1 << 32) for _ in range(npartitions)]\n\n    name = \"mimesis-\" + tokenize(\n        field, schema, npartitions, records_per_partition, seed\n    )\n    dsk = {\n        (name, i): (_generate_mimesis, field, schema, records_per_partition, seed)\n        for i, seed in enumerate(seeds)\n    }\n\n    return db.Bag(dsk, name, npartitions)\n\n\ndef make_people(npartitions=10, records_per_partition=1000, seed=None, locale=\"en\"):\n    \"\"\"Make a dataset of random people\n\n    This makes a Dask Bag with dictionary records of randomly generated people.\n    This requires the optional library ``mimesis`` to generate records.\n\n    Parameters\n    ----------\n    npartitions : int\n        Number of partitions\n    records_per_partition : int\n        Number of records in each partition\n    seed : int, (optional)\n        Random seed\n    locale : str\n        Language locale, like 'en', 'fr', 'zh', or 'ru'\n\n    Returns\n    -------\n    b: Dask Bag\n    \"\"\"\n    import_required(\n        \"mimesis\",\n        \"The mimesis module is required for this function.  Try:\\n\"\n        \"  python -m pip install mimesis\",\n    )\n\n    schema = lambda field: {\n        \"age\": field(\"random.randint\", a=0, b=120),\n        \"name\": (field(\"person.name\"), field(\"person.surname\")),\n        \"occupation\": field(\"person.occupation\"),\n        \"telephone\": field(\"person.telephone\"),\n        \"address\": {\"address\": field(\"address.address\"), \"city\": field(\"address.city\")},\n        \"credit-card\": {\n            \"number\": field(\"payment.credit_card_number\"),\n            \"expiration-date\": field(\"payment.credit_card_expiration_date\"),\n        },\n    }\n\n    return _make_mimesis(\n        {\"locale\": locale}, schema, npartitions, records_per_partition, seed\n    )\n", "path": "dask/datasets.py"}]}