We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Remove `pyhf.simplemodels.hepdata_like` from public API for v0.7.0
### Summary

[`pyhf.simplemodels.hepdata_like`](https://pyhf.readthedocs.io/en/v0.6.3/_generated/pyhf.simplemodels.hepdata_like.html#pyhf.simplemodels.hepdata_like) has been scheduled for removal from the public API in release `v0.7.0`. As `v0.7.0` will be the next release (and hopefully soon) this should get removed now.

### Additional Information

`pyhf.simplemodels.hepdata_like` has been deprecated since `v0.6.2`.

### Code of Conduct

- [X] I agree to follow the Code of Conduct

--- 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 warnings import warn
2 
3 from pyhf import Model
4 
5 __all__ = ["correlated_background", "uncorrelated_background"]
6 
7 
8 def __dir__():
9     return __all__
10 
11 
12 def correlated_background(signal, bkg, bkg_up, bkg_down, batch_size=None):
13     r"""
14     Construct a simple single channel :class:`~pyhf.pdf.Model` with a
15     :class:`~pyhf.modifiers.histosys` modifier representing a background
16     with a fully correlated bin-by-bin uncertainty.
17 
18     Args:
19         signal (:obj:`list`): The data in the signal sample.
20         bkg (:obj:`list`): The data in the background sample.
21         bkg_up (:obj:`list`): The background sample under an upward variation
22          corresponding to :math:`\alpha=+1`.
23         bkg_down (:obj:`list`): The background sample under a downward variation
24          corresponding to :math:`\alpha=-1`.
25         batch_size (:obj:`None` or :obj:`int`): Number of simultaneous (batched) Models to compute.
26 
27     Returns:
28         ~pyhf.pdf.Model: The statistical model adhering to the :obj:`model.json` schema.
29 
30     Example:
31         >>> import pyhf
32         >>> pyhf.set_backend("numpy")
33         >>> model = pyhf.simplemodels.correlated_background(
34         ...     signal=[12.0, 11.0],
35         ...     bkg=[50.0, 52.0],
36         ...     bkg_up=[45.0, 57.0],
37         ...     bkg_down=[55.0, 47.0],
38         ... )
39         >>> model.schema
40         'model.json'
41         >>> model.config.channels
42         ['single_channel']
43         >>> model.config.samples
44         ['background', 'signal']
45         >>> model.config.parameters
46         ['correlated_bkg_uncertainty', 'mu']
47         >>> model.expected_data(model.config.suggested_init())
48         array([62., 63.,  0.])
49 
50     """
51     spec = {
52         "channels": [
53             {
54                 "name": "single_channel",
55                 "samples": [
56                     {
57                         "name": "signal",
58                         "data": signal,
59                         "modifiers": [
60                             {"name": "mu", "type": "normfactor", "data": None}
61                         ],
62                     },
63                     {
64                         "name": "background",
65                         "data": bkg,
66                         "modifiers": [
67                             {
68                                 "name": "correlated_bkg_uncertainty",
69                                 "type": "histosys",
70                                 "data": {"hi_data": bkg_up, "lo_data": bkg_down},
71                             }
72                         ],
73                     },
74                 ],
75             }
76         ]
77     }
78     return Model(spec, batch_size=batch_size)
79 
80 
81 def uncorrelated_background(signal, bkg, bkg_uncertainty, batch_size=None):
82     """
83     Construct a simple single channel :class:`~pyhf.pdf.Model` with a
84     :class:`~pyhf.modifiers.shapesys` modifier representing an uncorrelated
85     background uncertainty.
86 
87     Example:
88         >>> import pyhf
89         >>> pyhf.set_backend("numpy")
90         >>> model = pyhf.simplemodels.uncorrelated_background(
91         ...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0]
92         ... )
93         >>> model.schema
94         'model.json'
95         >>> model.config.channels
96         ['singlechannel']
97         >>> model.config.samples
98         ['background', 'signal']
99         >>> model.config.parameters
100         ['mu', 'uncorr_bkguncrt']
101         >>> model.expected_data(model.config.suggested_init())
102         array([ 62.        ,  63.        , 277.77777778,  55.18367347])
103 
104     Args:
105         signal (:obj:`list`): The data in the signal sample
106         bkg (:obj:`list`): The data in the background sample
107         bkg_uncertainty (:obj:`list`): The statistical uncertainty on the background sample counts
108         batch_size (:obj:`None` or :obj:`int`): Number of simultaneous (batched) Models to compute
109 
110     Returns:
111         ~pyhf.pdf.Model: The statistical model adhering to the :obj:`model.json` schema
112 
113     """
114     spec = {
115         'channels': [
116             {
117                 'name': 'singlechannel',
118                 'samples': [
119                     {
120                         'name': 'signal',
121                         'data': signal,
122                         'modifiers': [
123                             {'name': 'mu', 'type': 'normfactor', 'data': None}
124                         ],
125                     },
126                     {
127                         'name': 'background',
128                         'data': bkg,
129                         'modifiers': [
130                             {
131                                 'name': 'uncorr_bkguncrt',
132                                 'type': 'shapesys',
133                                 'data': bkg_uncertainty,
134                             }
135                         ],
136                     },
137                 ],
138             }
139         ]
140     }
141     return Model(spec, batch_size=batch_size)
142 
143 
144 # Deprecated APIs
145 def _deprecated_api_warning(
146     deprecated_api, new_api, deprecated_release, remove_release
147 ):
148     warn(
149         f"{deprecated_api} is deprecated in favor of {new_api} as of pyhf v{deprecated_release} and will be removed in release {remove_release}."
150         + f" Please use {new_api}.",
151         DeprecationWarning,
152         stacklevel=3,  # Raise to user level
153     )
154 
155 
156 def hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):
157     """
158     .. note:: Deprecated API: Use :func:`~pyhf.simplemodels.uncorrelated_background`
159      instead.
160 
161     .. warning:: :func:`~pyhf.simplemodels.hepdata_like` will be removed in
162      ``pyhf`` ``v0.7.0``.
163     """
164     _deprecated_api_warning(
165         "pyhf.simplemodels.hepdata_like",
166         "pyhf.simplemodels.uncorrelated_background",
167         "0.6.2",
168         "0.7.0",
169     )
170     return uncorrelated_background(signal_data, bkg_data, bkg_uncerts, batch_size)
171
```
--- 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
@@ -151,20 +151,3 @@
         DeprecationWarning,
         stacklevel=3,  # Raise to user level
     )
-
-
-def hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):
-    """
-    .. note:: Deprecated API: Use :func:`~pyhf.simplemodels.uncorrelated_background`
-     instead.
-
-    .. warning:: :func:`~pyhf.simplemodels.hepdata_like` will be removed in
-     ``pyhf`` ``v0.7.0``.
-    """
-    _deprecated_api_warning(
-        "pyhf.simplemodels.hepdata_like",
-        "pyhf.simplemodels.uncorrelated_background",
-        "0.6.2",
-        "0.7.0",
-    )
-    return uncorrelated_background(signal_data, bkg_data, bkg_uncerts, batch_size)

{"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@@ -151,20 +151,3 @@\n         DeprecationWarning,\n         stacklevel=3,  # Raise to user level\n     )\n-\n-\n-def hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):\n-    \"\"\"\n-    .. note:: Deprecated API: Use :func:`~pyhf.simplemodels.uncorrelated_background`\n-     instead.\n-\n-    .. warning:: :func:`~pyhf.simplemodels.hepdata_like` will be removed in\n-     ``pyhf`` ``v0.7.0``.\n-    \"\"\"\n-    _deprecated_api_warning(\n-        \"pyhf.simplemodels.hepdata_like\",\n-        \"pyhf.simplemodels.uncorrelated_background\",\n-        \"0.6.2\",\n-        \"0.7.0\",\n-    )\n-    return uncorrelated_background(signal_data, bkg_data, bkg_uncerts, batch_size)\n", "issue": "Remove `pyhf.simplemodels.hepdata_like` from public API for v0.7.0\n### Summary\n\n[`pyhf.simplemodels.hepdata_like`](https://pyhf.readthedocs.io/en/v0.6.3/_generated/pyhf.simplemodels.hepdata_like.html#pyhf.simplemodels.hepdata_like) has been scheduled for removal from the public API in release `v0.7.0`. As `v0.7.0` will be the next release (and hopefully soon) this should get removed now.\n\n### Additional Information\n\n`pyhf.simplemodels.hepdata_like` has been deprecated since `v0.6.2`.\n\n### Code of Conduct\n\n- [X] I agree to follow the Code of Conduct\n", "before_files": [{"content": "from warnings import warn\n\nfrom pyhf import Model\n\n__all__ = [\"correlated_background\", \"uncorrelated_background\"]\n\n\ndef __dir__():\n    return __all__\n\n\ndef correlated_background(signal, bkg, bkg_up, bkg_down, batch_size=None):\n    r\"\"\"\n    Construct a simple single channel :class:`~pyhf.pdf.Model` with a\n    :class:`~pyhf.modifiers.histosys` modifier representing a background\n    with a fully correlated bin-by-bin uncertainty.\n\n    Args:\n        signal (:obj:`list`): The data in the signal sample.\n        bkg (:obj:`list`): The data in the background sample.\n        bkg_up (:obj:`list`): The background sample under an upward variation\n         corresponding to :math:`\\alpha=+1`.\n        bkg_down (:obj:`list`): The background sample under a downward variation\n         corresponding to :math:`\\alpha=-1`.\n        batch_size (:obj:`None` or :obj:`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    Example:\n        >>> import pyhf\n        >>> pyhf.set_backend(\"numpy\")\n        >>> model = pyhf.simplemodels.correlated_background(\n        ...     signal=[12.0, 11.0],\n        ...     bkg=[50.0, 52.0],\n        ...     bkg_up=[45.0, 57.0],\n        ...     bkg_down=[55.0, 47.0],\n        ... )\n        >>> model.schema\n        'model.json'\n        >>> model.config.channels\n        ['single_channel']\n        >>> model.config.samples\n        ['background', 'signal']\n        >>> model.config.parameters\n        ['correlated_bkg_uncertainty', 'mu']\n        >>> model.expected_data(model.config.suggested_init())\n        array([62., 63.,  0.])\n\n    \"\"\"\n    spec = {\n        \"channels\": [\n            {\n                \"name\": \"single_channel\",\n                \"samples\": [\n                    {\n                        \"name\": \"signal\",\n                        \"data\": signal,\n                        \"modifiers\": [\n                            {\"name\": \"mu\", \"type\": \"normfactor\", \"data\": None}\n                        ],\n                    },\n                    {\n                        \"name\": \"background\",\n                        \"data\": bkg,\n                        \"modifiers\": [\n                            {\n                                \"name\": \"correlated_bkg_uncertainty\",\n                                \"type\": \"histosys\",\n                                \"data\": {\"hi_data\": bkg_up, \"lo_data\": bkg_down},\n                            }\n                        ],\n                    },\n                ],\n            }\n        ]\n    }\n    return Model(spec, batch_size=batch_size)\n\n\ndef uncorrelated_background(signal, bkg, bkg_uncertainty, 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.uncorrelated_background(\n        ...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[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 (:obj:`list`): The data in the signal sample\n        bkg (:obj:`list`): The data in the background sample\n        bkg_uncertainty (:obj:`list`): The statistical uncertainty on the background sample counts\n        batch_size (:obj:`None` or :obj:`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,\n                        'modifiers': [\n                            {'name': 'mu', 'type': 'normfactor', 'data': None}\n                        ],\n                    },\n                    {\n                        'name': 'background',\n                        'data': bkg,\n                        'modifiers': [\n                            {\n                                'name': 'uncorr_bkguncrt',\n                                'type': 'shapesys',\n                                'data': bkg_uncertainty,\n                            }\n                        ],\n                    },\n                ],\n            }\n        ]\n    }\n    return Model(spec, batch_size=batch_size)\n\n\n# Deprecated APIs\ndef _deprecated_api_warning(\n    deprecated_api, new_api, deprecated_release, remove_release\n):\n    warn(\n        f\"{deprecated_api} is deprecated in favor of {new_api} as of pyhf v{deprecated_release} and will be removed in release {remove_release}.\"\n        + f\" Please use {new_api}.\",\n        DeprecationWarning,\n        stacklevel=3,  # Raise to user level\n    )\n\n\ndef hepdata_like(signal_data, bkg_data, bkg_uncerts, batch_size=None):\n    \"\"\"\n    .. note:: Deprecated API: Use :func:`~pyhf.simplemodels.uncorrelated_background`\n     instead.\n\n    .. warning:: :func:`~pyhf.simplemodels.hepdata_like` will be removed in\n     ``pyhf`` ``v0.7.0``.\n    \"\"\"\n    _deprecated_api_warning(\n        \"pyhf.simplemodels.hepdata_like\",\n        \"pyhf.simplemodels.uncorrelated_background\",\n        \"0.6.2\",\n        \"0.7.0\",\n    )\n    return uncorrelated_background(signal_data, bkg_data, bkg_uncerts, batch_size)\n", "path": "src/pyhf/simplemodels.py"}], "after_files": [{"content": "from warnings import warn\n\nfrom pyhf import Model\n\n__all__ = [\"correlated_background\", \"uncorrelated_background\"]\n\n\ndef __dir__():\n    return __all__\n\n\ndef correlated_background(signal, bkg, bkg_up, bkg_down, batch_size=None):\n    r\"\"\"\n    Construct a simple single channel :class:`~pyhf.pdf.Model` with a\n    :class:`~pyhf.modifiers.histosys` modifier representing a background\n    with a fully correlated bin-by-bin uncertainty.\n\n    Args:\n        signal (:obj:`list`): The data in the signal sample.\n        bkg (:obj:`list`): The data in the background sample.\n        bkg_up (:obj:`list`): The background sample under an upward variation\n         corresponding to :math:`\\alpha=+1`.\n        bkg_down (:obj:`list`): The background sample under a downward variation\n         corresponding to :math:`\\alpha=-1`.\n        batch_size (:obj:`None` or :obj:`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    Example:\n        >>> import pyhf\n        >>> pyhf.set_backend(\"numpy\")\n        >>> model = pyhf.simplemodels.correlated_background(\n        ...     signal=[12.0, 11.0],\n        ...     bkg=[50.0, 52.0],\n        ...     bkg_up=[45.0, 57.0],\n        ...     bkg_down=[55.0, 47.0],\n        ... )\n        >>> model.schema\n        'model.json'\n        >>> model.config.channels\n        ['single_channel']\n        >>> model.config.samples\n        ['background', 'signal']\n        >>> model.config.parameters\n        ['correlated_bkg_uncertainty', 'mu']\n        >>> model.expected_data(model.config.suggested_init())\n        array([62., 63.,  0.])\n\n    \"\"\"\n    spec = {\n        \"channels\": [\n            {\n                \"name\": \"single_channel\",\n                \"samples\": [\n                    {\n                        \"name\": \"signal\",\n                        \"data\": signal,\n                        \"modifiers\": [\n                            {\"name\": \"mu\", \"type\": \"normfactor\", \"data\": None}\n                        ],\n                    },\n                    {\n                        \"name\": \"background\",\n                        \"data\": bkg,\n                        \"modifiers\": [\n                            {\n                                \"name\": \"correlated_bkg_uncertainty\",\n                                \"type\": \"histosys\",\n                                \"data\": {\"hi_data\": bkg_up, \"lo_data\": bkg_down},\n                            }\n                        ],\n                    },\n                ],\n            }\n        ]\n    }\n    return Model(spec, batch_size=batch_size)\n\n\ndef uncorrelated_background(signal, bkg, bkg_uncertainty, 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.uncorrelated_background(\n        ...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[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 (:obj:`list`): The data in the signal sample\n        bkg (:obj:`list`): The data in the background sample\n        bkg_uncertainty (:obj:`list`): The statistical uncertainty on the background sample counts\n        batch_size (:obj:`None` or :obj:`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,\n                        'modifiers': [\n                            {'name': 'mu', 'type': 'normfactor', 'data': None}\n                        ],\n                    },\n                    {\n                        'name': 'background',\n                        'data': bkg,\n                        'modifiers': [\n                            {\n                                'name': 'uncorr_bkguncrt',\n                                'type': 'shapesys',\n                                'data': bkg_uncertainty,\n                            }\n                        ],\n                    },\n                ],\n            }\n        ]\n    }\n    return Model(spec, batch_size=batch_size)\n\n\n# Deprecated APIs\ndef _deprecated_api_warning(\n    deprecated_api, new_api, deprecated_release, remove_release\n):\n    warn(\n        f\"{deprecated_api} is deprecated in favor of {new_api} as of pyhf v{deprecated_release} and will be removed in release {remove_release}.\"\n        + f\" Please use {new_api}.\",\n        DeprecationWarning,\n        stacklevel=3,  # Raise to user level\n    )\n", "path": "src/pyhf/simplemodels.py"}]}