prompt: 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()
  
  ```
  
golden_diff: 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))
  
verification_info: {"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"}]}