We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Document simplemodels API
# Description

In discussion today with @coolalexzb, I realized that the [`pyhf.simplemodels`](https://github.com/scikit-hep/pyhf/blob/79984be837ef6e53bdd12a82163c34d47d507dba/src/pyhf/simplemodels.py) API is not documented in our docs. Even thought this isn't something we want people to really use, we still show it in our examples and so it needs documentation.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/pyhf/simplemodels.py`
Content:
```
1 from . import Model
2 
3 
4 def hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):
5     spec = {
6         'channels': [
7             {
8                 'name': 'singlechannel',
9                 'samples': [
10                     {
11                         'name': 'signal',
12                         'data': signal_data,
13                         'modifiers': [
14                             {'name': 'mu', 'type': 'normfactor', 'data': None}
15                         ],
16                     },
17                     {
18                         'name': 'background',
19                         'data': bkg_data,
20                         'modifiers': [
21                             {
22                                 'name': 'uncorr_bkguncrt',
23                                 'type': 'shapesys',
24                                 'data': bkg_uncerts,
25                             }
26                         ],
27                     },
28                 ],
29             }
30         ]
31     }
32     return Model(spec, batch_size=batch_size)
33
```
--- 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/src/pyhf/simplemodels.py b/src/pyhf/simplemodels.py
--- a/src/pyhf/simplemodels.py
+++ b/src/pyhf/simplemodels.py
@@ -2,6 +2,38 @@
 
 
 def hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):
+    """
+    Construct a simple single channel :class:`~pyhf.pdf.Model` with a
+    :class:`~pyhf.modifiers.shapesys` modifier representing an uncorrelated
+    background uncertainty.
+
+    Example:
+        >>> import pyhf
+        >>> pyhf.set_backend("numpy")
+        >>> model = pyhf.simplemodels.hepdata_like(
+        ...     signal_data=[12.0, 11.0], bkg_data=[50.0, 52.0], bkg_uncerts=[3.0, 7.0]
+        ... )
+        >>> model.schema
+        'model.json'
+        >>> model.config.channels
+        ['singlechannel']
+        >>> model.config.samples
+        ['background', 'signal']
+        >>> model.config.parameters
+        ['mu', 'uncorr_bkguncrt']
+        >>> model.expected_data(model.config.suggested_init())
+        array([ 62.        ,  63.        , 277.77777778,  55.18367347])
+
+    Args:
+        signal_data (`list`): The data in the signal sample
+        bkg_data (`list`): The data in the background sample
+        bkg_uncerts (`list`): The statistical uncertainty on the background sample counts
+        batch_size (`None` or `int`): Number of simultaneous (batched) Models to compute
+
+    Returns:
+        ~pyhf.pdf.Model: The statistical model adhering to the :obj:`model.json` schema
+
+    """
     spec = {
         'channels': [
             {

{"golden_diff": "diff --git a/src/pyhf/simplemodels.py b/src/pyhf/simplemodels.py\n--- a/src/pyhf/simplemodels.py\n+++ b/src/pyhf/simplemodels.py\n@@ -2,6 +2,38 @@\n \n \n def hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):\n+    \"\"\"\n+    Construct a simple single channel :class:`~pyhf.pdf.Model` with a\n+    :class:`~pyhf.modifiers.shapesys` modifier representing an uncorrelated\n+    background uncertainty.\n+\n+    Example:\n+        >>> import pyhf\n+        >>> pyhf.set_backend(\"numpy\")\n+        >>> model = pyhf.simplemodels.hepdata_like(\n+        ...     signal_data=[12.0, 11.0], bkg_data=[50.0, 52.0], bkg_uncerts=[3.0, 7.0]\n+        ... )\n+        >>> model.schema\n+        'model.json'\n+        >>> model.config.channels\n+        ['singlechannel']\n+        >>> model.config.samples\n+        ['background', 'signal']\n+        >>> model.config.parameters\n+        ['mu', 'uncorr_bkguncrt']\n+        >>> model.expected_data(model.config.suggested_init())\n+        array([ 62.        ,  63.        , 277.77777778,  55.18367347])\n+\n+    Args:\n+        signal_data (`list`): The data in the signal sample\n+        bkg_data (`list`): The data in the background sample\n+        bkg_uncerts (`list`): The statistical uncertainty on the background sample counts\n+        batch_size (`None` or `int`): Number of simultaneous (batched) Models to compute\n+\n+    Returns:\n+        ~pyhf.pdf.Model: The statistical model adhering to the :obj:`model.json` schema\n+\n+    \"\"\"\n     spec = {\n         'channels': [\n             {\n", "issue": "Document simplemodels API\n# Description\r\n\r\nIn discussion today with @coolalexzb, I realized that the [`pyhf.simplemodels`](https://github.com/scikit-hep/pyhf/blob/79984be837ef6e53bdd12a82163c34d47d507dba/src/pyhf/simplemodels.py) API is not documented in our docs. Even thought this isn't something we want people to really use, we still show it in our examples and so it needs documentation.\n", "before_files": [{"content": "from . import Model\n\n\ndef hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):\n    spec = {\n        'channels': [\n            {\n                'name': 'singlechannel',\n                'samples': [\n                    {\n                        'name': 'signal',\n                        'data': signal_data,\n                        'modifiers': [\n                            {'name': 'mu', 'type': 'normfactor', 'data': None}\n                        ],\n                    },\n                    {\n                        'name': 'background',\n                        'data': bkg_data,\n                        'modifiers': [\n                            {\n                                'name': 'uncorr_bkguncrt',\n                                'type': 'shapesys',\n                                'data': bkg_uncerts,\n                            }\n                        ],\n                    },\n                ],\n            }\n        ]\n    }\n    return Model(spec, batch_size=batch_size)\n", "path": "src/pyhf/simplemodels.py"}], "after_files": [{"content": "from . import Model\n\n\ndef hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):\n    \"\"\"\n    Construct a simple single channel :class:`~pyhf.pdf.Model` with a\n    :class:`~pyhf.modifiers.shapesys` modifier representing an uncorrelated\n    background uncertainty.\n\n    Example:\n        >>> import pyhf\n        >>> pyhf.set_backend(\"numpy\")\n        >>> model = pyhf.simplemodels.hepdata_like(\n        ...     signal_data=[12.0, 11.0], bkg_data=[50.0, 52.0], bkg_uncerts=[3.0, 7.0]\n        ... )\n        >>> model.schema\n        'model.json'\n        >>> model.config.channels\n        ['singlechannel']\n        >>> model.config.samples\n        ['background', 'signal']\n        >>> model.config.parameters\n        ['mu', 'uncorr_bkguncrt']\n        >>> model.expected_data(model.config.suggested_init())\n        array([ 62.        ,  63.        , 277.77777778,  55.18367347])\n\n    Args:\n        signal_data (`list`): The data in the signal sample\n        bkg_data (`list`): The data in the background sample\n        bkg_uncerts (`list`): The statistical uncertainty on the background sample counts\n        batch_size (`None` or `int`): Number of simultaneous (batched) Models to compute\n\n    Returns:\n        ~pyhf.pdf.Model: The statistical model adhering to the :obj:`model.json` schema\n\n    \"\"\"\n    spec = {\n        'channels': [\n            {\n                'name': 'singlechannel',\n                'samples': [\n                    {\n                        'name': 'signal',\n                        'data': signal_data,\n                        'modifiers': [\n                            {'name': 'mu', 'type': 'normfactor', 'data': None}\n                        ],\n                    },\n                    {\n                        'name': 'background',\n                        'data': bkg_data,\n                        'modifiers': [\n                            {\n                                'name': 'uncorr_bkguncrt',\n                                'type': 'shapesys',\n                                'data': bkg_uncerts,\n                            }\n                        ],\n                    },\n                ],\n            }\n        ]\n    }\n    return Model(spec, batch_size=batch_size)\n", "path": "src/pyhf/simplemodels.py"}]}