We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
`ak.type` does not accept "anything `ak.to_layout` recognizes"
### Version of Awkward Array

main

### Description and code to reproduce

```python
>>> import awkward as ak
>>> ak.type([1,2,3])

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[12], line 1
----> 1 ak.type([1,2,3])

File /lib/python3.10/site-packages/awkward/operations/ak_type.py:77, in type(array)
     15 """
     16 Args:
     17     array: Array-like data (anything #ak.to_layout recognizes).
   (...)
     71 to the language.)
     72 """
     73 with ak._errors.OperationErrorContext(
     74     "ak.type",
     75     dict(array=array),
     76 ):
---> 77     return _impl(array)

File /lib/python3.10/site-packages/awkward/operations/ak_type.py:144, in _impl(array)
    141     return array.form.type
    143 else:
--> 144     raise ak._errors.wrap_error(TypeError(f"unrecognized array type: {array!r}"))

TypeError: while calling

    ak.type(
        array = [1, 2, 3]
    )

Error details: unrecognized array type: [1, 2, 3]
```

--- 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/awkward/operations/ak_type.py`
Content:
```
1 # BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE
2 
3 import builtins
4 import numbers
5 from datetime import datetime, timedelta
6 
7 from awkward_cpp.lib import _ext
8 
9 import awkward as ak
10 
11 np = ak._nplikes.NumpyMetadata.instance()
12 
13 
14 def type(array):
15     """
16     Args:
17         array: Array-like data (anything #ak.to_layout recognizes).
18 
19     The high-level type of an `array` (many types supported, including all
20     Awkward Arrays and Records) as #ak.types.Type objects.
21 
22     The high-level type ignores layout differences like
23     #ak.contents.ListArray versus #ak.contents.ListOffsetArray, but
24     not differences like "regular-sized lists" (i.e.
25     #ak.contents.RegularArray) versus "variable-sized lists" (i.e.
26     #ak.contents.ListArray and similar).
27 
28     Types are rendered as [Datashape](https://datashape.readthedocs.io/)
29     strings, which makes the same distinctions.
30 
31     For example,
32 
33         >>> array = ak.Array([[{"x": 1.1, "y": [1]}, {"x": 2.2, "y": [2, 2]}],
34         ...                   [],
35         ...                   [{"x": 3.3, "y": [3, 3, 3]}]])
36 
37     has type
38 
39         >>> ak.type(array).show()
40         3 * var * {
41             x: float64,
42             y: var * int64
43         }
44 
45     but
46 
47         >>> array = ak.Array(np.arange(2*3*5).reshape(2, 3, 5))
48 
49     has type
50 
51         >>> ak.type(array).show()
52         2 * 3 * 5 * int64
53 
54     Some cases, like heterogeneous data, require [extensions beyond the
55     Datashape specification](https://github.com/blaze/datashape/issues/237).
56     For example,
57 
58         >>> array = ak.Array([1, "two", [3, 3, 3]])
59 
60     has type
61 
62         >>> ak.type(array).show()
63         3 * union[
64             int64,
65             string,
66             var * int64
67         ]
68 
69     but "union" is not a Datashape type-constructor. (Its syntax is
70     similar to existing type-constructors, so it's a plausible addition
71     to the language.)
72     """
73     with ak._errors.OperationErrorContext(
74         "ak.type",
75         dict(array=array),
76     ):
77         return _impl(array)
78 
79 
80 def _impl(array):
81     if array is None:
82         return ak.types.UnknownType()
83 
84     elif isinstance(array, np.dtype):
85         return ak.types.NumpyType(ak.types.numpytype.dtype_to_primitive(array))
86 
87     elif (
88         isinstance(array, np.generic)
89         or isinstance(array, builtins.type)
90         and issubclass(array, np.generic)
91     ):
92         primitive = ak.types.numpytype.dtype_to_primitive(np.dtype(array))
93         return ak.types.NumpyType(primitive)
94 
95     elif isinstance(array, bool):  # np.bool_ in np.generic (above)
96         return ak.types.NumpyType("bool")
97 
98     elif isinstance(array, numbers.Integral):
99         return ak.types.NumpyType("int64")
100 
101     elif isinstance(array, numbers.Real):
102         return ak.types.NumpyType("float64")
103 
104     elif isinstance(array, numbers.Complex):
105         return ak.types.NumpyType("complex128")
106 
107     elif isinstance(array, datetime):  # np.datetime64 in np.generic (above)
108         return ak.types.NumpyType("datetime64")
109 
110     elif isinstance(array, timedelta):  # np.timedelta64 in np.generic (above)
111         return ak.types.NumpyType("timedelta")
112 
113     elif isinstance(
114         array,
115         (
116             ak.highlevel.Array,
117             ak.highlevel.Record,
118             ak.highlevel.ArrayBuilder,
119         ),
120     ):
121         return array.type
122 
123     elif isinstance(array, np.ndarray):
124         if len(array.shape) == 0:
125             return _impl(array.reshape((1,))[0])
126         else:
127             primitive = ak.types.numpytype.dtype_to_primitive(array.dtype)
128             out = ak.types.NumpyType(primitive)
129             for x in array.shape[-1:0:-1]:
130                 out = ak.types.RegularType(out, x)
131             return ak.types.ArrayType(out, array.shape[0])
132 
133     elif isinstance(array, _ext.ArrayBuilder):
134         form = ak.forms.from_json(array.form())
135         return ak.types.ArrayType(form.type_from_behavior(None), len(array))
136 
137     elif isinstance(array, ak.record.Record):
138         return array.array.form.type
139 
140     elif isinstance(array, ak.contents.Content):
141         return array.form.type
142 
143     else:
144         raise ak._errors.wrap_error(TypeError(f"unrecognized array type: {array!r}"))
145
```
--- 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/awkward/operations/ak_type.py b/src/awkward/operations/ak_type.py
--- a/src/awkward/operations/ak_type.py
+++ b/src/awkward/operations/ak_type.py
@@ -141,4 +141,5 @@
         return array.form.type
 
     else:
-        raise ak._errors.wrap_error(TypeError(f"unrecognized array type: {array!r}"))
+        layout = ak.to_layout(array, allow_other=False)
+        return _impl(ak._util.wrap(layout))

{"golden_diff": "diff --git a/src/awkward/operations/ak_type.py b/src/awkward/operations/ak_type.py\n--- a/src/awkward/operations/ak_type.py\n+++ b/src/awkward/operations/ak_type.py\n@@ -141,4 +141,5 @@\n         return array.form.type\n \n     else:\n-        raise ak._errors.wrap_error(TypeError(f\"unrecognized array type: {array!r}\"))\n+        layout = ak.to_layout(array, allow_other=False)\n+        return _impl(ak._util.wrap(layout))\n", "issue": "`ak.type` does not accept \"anything `ak.to_layout` recognizes\"\n### Version of Awkward Array\n\nmain\n\n### Description and code to reproduce\n\n```python\r\n>>> import awkward as ak\r\n>>> ak.type([1,2,3])\r\n\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\nCell In[12], line 1\r\n----> 1 ak.type([1,2,3])\r\n\r\nFile /lib/python3.10/site-packages/awkward/operations/ak_type.py:77, in type(array)\r\n     15 \"\"\"\r\n     16 Args:\r\n     17     array: Array-like data (anything #ak.to_layout recognizes).\r\n   (...)\r\n     71 to the language.)\r\n     72 \"\"\"\r\n     73 with ak._errors.OperationErrorContext(\r\n     74     \"ak.type\",\r\n     75     dict(array=array),\r\n     76 ):\r\n---> 77     return _impl(array)\r\n\r\nFile /lib/python3.10/site-packages/awkward/operations/ak_type.py:144, in _impl(array)\r\n    141     return array.form.type\r\n    143 else:\r\n--> 144     raise ak._errors.wrap_error(TypeError(f\"unrecognized array type: {array!r}\"))\r\n\r\nTypeError: while calling\r\n\r\n    ak.type(\r\n        array = [1, 2, 3]\r\n    )\r\n\r\nError details: unrecognized array type: [1, 2, 3]\r\n```\n", "before_files": [{"content": "# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE\n\nimport builtins\nimport numbers\nfrom datetime import datetime, timedelta\n\nfrom awkward_cpp.lib import _ext\n\nimport awkward as ak\n\nnp = ak._nplikes.NumpyMetadata.instance()\n\n\ndef type(array):\n    \"\"\"\n    Args:\n        array: Array-like data (anything #ak.to_layout recognizes).\n\n    The high-level type of an `array` (many types supported, including all\n    Awkward Arrays and Records) as #ak.types.Type objects.\n\n    The high-level type ignores layout differences like\n    #ak.contents.ListArray versus #ak.contents.ListOffsetArray, but\n    not differences like \"regular-sized lists\" (i.e.\n    #ak.contents.RegularArray) versus \"variable-sized lists\" (i.e.\n    #ak.contents.ListArray and similar).\n\n    Types are rendered as [Datashape](https://datashape.readthedocs.io/)\n    strings, which makes the same distinctions.\n\n    For example,\n\n        >>> array = ak.Array([[{\"x\": 1.1, \"y\": [1]}, {\"x\": 2.2, \"y\": [2, 2]}],\n        ...                   [],\n        ...                   [{\"x\": 3.3, \"y\": [3, 3, 3]}]])\n\n    has type\n\n        >>> ak.type(array).show()\n        3 * var * {\n            x: float64,\n            y: var * int64\n        }\n\n    but\n\n        >>> array = ak.Array(np.arange(2*3*5).reshape(2, 3, 5))\n\n    has type\n\n        >>> ak.type(array).show()\n        2 * 3 * 5 * int64\n\n    Some cases, like heterogeneous data, require [extensions beyond the\n    Datashape specification](https://github.com/blaze/datashape/issues/237).\n    For example,\n\n        >>> array = ak.Array([1, \"two\", [3, 3, 3]])\n\n    has type\n\n        >>> ak.type(array).show()\n        3 * union[\n            int64,\n            string,\n            var * int64\n        ]\n\n    but \"union\" is not a Datashape type-constructor. (Its syntax is\n    similar to existing type-constructors, so it's a plausible addition\n    to the language.)\n    \"\"\"\n    with ak._errors.OperationErrorContext(\n        \"ak.type\",\n        dict(array=array),\n    ):\n        return _impl(array)\n\n\ndef _impl(array):\n    if array is None:\n        return ak.types.UnknownType()\n\n    elif isinstance(array, np.dtype):\n        return ak.types.NumpyType(ak.types.numpytype.dtype_to_primitive(array))\n\n    elif (\n        isinstance(array, np.generic)\n        or isinstance(array, builtins.type)\n        and issubclass(array, np.generic)\n    ):\n        primitive = ak.types.numpytype.dtype_to_primitive(np.dtype(array))\n        return ak.types.NumpyType(primitive)\n\n    elif isinstance(array, bool):  # np.bool_ in np.generic (above)\n        return ak.types.NumpyType(\"bool\")\n\n    elif isinstance(array, numbers.Integral):\n        return ak.types.NumpyType(\"int64\")\n\n    elif isinstance(array, numbers.Real):\n        return ak.types.NumpyType(\"float64\")\n\n    elif isinstance(array, numbers.Complex):\n        return ak.types.NumpyType(\"complex128\")\n\n    elif isinstance(array, datetime):  # np.datetime64 in np.generic (above)\n        return ak.types.NumpyType(\"datetime64\")\n\n    elif isinstance(array, timedelta):  # np.timedelta64 in np.generic (above)\n        return ak.types.NumpyType(\"timedelta\")\n\n    elif isinstance(\n        array,\n        (\n            ak.highlevel.Array,\n            ak.highlevel.Record,\n            ak.highlevel.ArrayBuilder,\n        ),\n    ):\n        return array.type\n\n    elif isinstance(array, np.ndarray):\n        if len(array.shape) == 0:\n            return _impl(array.reshape((1,))[0])\n        else:\n            primitive = ak.types.numpytype.dtype_to_primitive(array.dtype)\n            out = ak.types.NumpyType(primitive)\n            for x in array.shape[-1:0:-1]:\n                out = ak.types.RegularType(out, x)\n            return ak.types.ArrayType(out, array.shape[0])\n\n    elif isinstance(array, _ext.ArrayBuilder):\n        form = ak.forms.from_json(array.form())\n        return ak.types.ArrayType(form.type_from_behavior(None), len(array))\n\n    elif isinstance(array, ak.record.Record):\n        return array.array.form.type\n\n    elif isinstance(array, ak.contents.Content):\n        return array.form.type\n\n    else:\n        raise ak._errors.wrap_error(TypeError(f\"unrecognized array type: {array!r}\"))\n", "path": "src/awkward/operations/ak_type.py"}], "after_files": [{"content": "# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE\n\nimport builtins\nimport numbers\nfrom datetime import datetime, timedelta\n\nfrom awkward_cpp.lib import _ext\n\nimport awkward as ak\n\nnp = ak._nplikes.NumpyMetadata.instance()\n\n\ndef type(array):\n    \"\"\"\n    Args:\n        array: Array-like data (anything #ak.to_layout recognizes).\n\n    The high-level type of an `array` (many types supported, including all\n    Awkward Arrays and Records) as #ak.types.Type objects.\n\n    The high-level type ignores layout differences like\n    #ak.contents.ListArray versus #ak.contents.ListOffsetArray, but\n    not differences like \"regular-sized lists\" (i.e.\n    #ak.contents.RegularArray) versus \"variable-sized lists\" (i.e.\n    #ak.contents.ListArray and similar).\n\n    Types are rendered as [Datashape](https://datashape.readthedocs.io/)\n    strings, which makes the same distinctions.\n\n    For example,\n\n        >>> array = ak.Array([[{\"x\": 1.1, \"y\": [1]}, {\"x\": 2.2, \"y\": [2, 2]}],\n        ...                   [],\n        ...                   [{\"x\": 3.3, \"y\": [3, 3, 3]}]])\n\n    has type\n\n        >>> ak.type(array).show()\n        3 * var * {\n            x: float64,\n            y: var * int64\n        }\n\n    but\n\n        >>> array = ak.Array(np.arange(2*3*5).reshape(2, 3, 5))\n\n    has type\n\n        >>> ak.type(array).show()\n        2 * 3 * 5 * int64\n\n    Some cases, like heterogeneous data, require [extensions beyond the\n    Datashape specification](https://github.com/blaze/datashape/issues/237).\n    For example,\n\n        >>> array = ak.Array([1, \"two\", [3, 3, 3]])\n\n    has type\n\n        >>> ak.type(array).show()\n        3 * union[\n            int64,\n            string,\n            var * int64\n        ]\n\n    but \"union\" is not a Datashape type-constructor. (Its syntax is\n    similar to existing type-constructors, so it's a plausible addition\n    to the language.)\n    \"\"\"\n    with ak._errors.OperationErrorContext(\n        \"ak.type\",\n        dict(array=array),\n    ):\n        return _impl(array)\n\n\ndef _impl(array):\n    if array is None:\n        return ak.types.UnknownType()\n\n    elif isinstance(array, np.dtype):\n        return ak.types.NumpyType(ak.types.numpytype.dtype_to_primitive(array))\n\n    elif (\n        isinstance(array, np.generic)\n        or isinstance(array, builtins.type)\n        and issubclass(array, np.generic)\n    ):\n        primitive = ak.types.numpytype.dtype_to_primitive(np.dtype(array))\n        return ak.types.NumpyType(primitive)\n\n    elif isinstance(array, bool):  # np.bool_ in np.generic (above)\n        return ak.types.NumpyType(\"bool\")\n\n    elif isinstance(array, numbers.Integral):\n        return ak.types.NumpyType(\"int64\")\n\n    elif isinstance(array, numbers.Real):\n        return ak.types.NumpyType(\"float64\")\n\n    elif isinstance(array, numbers.Complex):\n        return ak.types.NumpyType(\"complex128\")\n\n    elif isinstance(array, datetime):  # np.datetime64 in np.generic (above)\n        return ak.types.NumpyType(\"datetime64\")\n\n    elif isinstance(array, timedelta):  # np.timedelta64 in np.generic (above)\n        return ak.types.NumpyType(\"timedelta\")\n\n    elif isinstance(\n        array,\n        (\n            ak.highlevel.Array,\n            ak.highlevel.Record,\n            ak.highlevel.ArrayBuilder,\n        ),\n    ):\n        return array.type\n\n    elif isinstance(array, np.ndarray):\n        if len(array.shape) == 0:\n            return _impl(array.reshape((1,))[0])\n        else:\n            primitive = ak.types.numpytype.dtype_to_primitive(array.dtype)\n            out = ak.types.NumpyType(primitive)\n            for x in array.shape[-1:0:-1]:\n                out = ak.types.RegularType(out, x)\n            return ak.types.ArrayType(out, array.shape[0])\n\n    elif isinstance(array, _ext.ArrayBuilder):\n        form = ak.forms.from_json(array.form())\n        return ak.types.ArrayType(form.type_from_behavior(None), len(array))\n\n    elif isinstance(array, ak.record.Record):\n        return array.array.form.type\n\n    elif isinstance(array, ak.contents.Content):\n        return array.form.type\n\n    else:\n        layout = ak.to_layout(array, allow_other=False)\n        return _impl(ak._util.wrap(layout))\n", "path": "src/awkward/operations/ak_type.py"}]}