prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Add some missing array methods 
  A few numpy array methods were discovered to be missing:
  ```
                  "loads",
                  "mafromtxt",
                  "matmul",
                  "ndfromtxt",
  ```
  
  These should be copied over as necessary, and the comparison blocklist updated. 
  
  ref: https://github.com/nv-legate/cunumeric/pull/224#discussion_r830657282
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `docs/cunumeric/source/comparison/_comparison_generator.py`
  Content:
  ```
  1 import importlib
  2 
  3 import numpy
  4 
  5 blocklist = [
  6     "abs",
  7     "add_docstring",
  8     "add_newdoc",
  9     "add_newdoc_ufunc",
  10     "alen",
  11     "alltrue",
  12     "bitwise_not",
  13     "compare_chararrays",
  14     "cumproduct",
  15     "fastCopyAndTranspose",
  16     "get_array_wrap",
  17     "iterable",
  18     "loads",
  19     "mafromtxt",
  20     "max",
  21     "min",
  22     "ndfromtxt",
  23     "ndim",
  24     "product",
  25     "recfromcsv",
  26     "recfromtxt",
  27     "round",
  28     "safe_eval",
  29     "set_numeric_ops",
  30     "size",
  31     "sometrue",
  32     "test",
  33 ]
  34 
  35 
  36 def check_ufunc(obj, n):
  37     try:
  38         return isinstance(getattr(obj, n), numpy.ufunc)
  39     except:  # noqa E722
  40         return False
  41 
  42 
  43 def _filter(obj, n, ufuncs=False):
  44     is_ufunc = check_ufunc(obj, n)
  45     if not ufuncs:
  46         is_ufunc = not is_ufunc
  47 
  48     try:
  49         return (
  50             n not in blocklist
  51             and callable(getattr(obj, n))  # callable
  52             and not isinstance(getattr(obj, n), type)  # not class
  53             and n[0].islower()  # starts with lower char
  54             and not n.startswith("__")  # not special methods
  55             and is_ufunc
  56         )
  57     except:  # noqa: E722
  58         return False
  59 
  60 
  61 def _get_functions(obj, ufuncs=False):
  62     return set([n for n in dir(obj) if (_filter(obj, n, ufuncs))])
  63 
  64 
  65 def _import(mod, klass):
  66     try:
  67         obj = importlib.import_module(mod)
  68     except ModuleNotFoundError:
  69         return None, None
  70 
  71     if klass:
  72         obj = getattr(obj, klass)
  73         return obj, ":meth:`{}.{}.{{}}`".format(mod, klass)
  74     else:
  75         # ufunc is not a function
  76         return obj, ":obj:`{}.{{}}`".format(mod)
  77 
  78 
  79 def _section(
  80     header, mod_ext, other_lib, klass=None, exclude_mod=None, ufuncs=False
  81 ):
  82     base_mod = "numpy" + mod_ext
  83     other_mod = other_lib + mod_ext
  84 
  85     base_funcs = []
  86     base_obj, base_fmt = _import(base_mod, klass)
  87     base_funcs = _get_functions(base_obj, ufuncs)
  88     lg_obj, lg_fmt = _import(other_mod, klass)
  89 
  90     lg_funcs = []
  91     for f in _get_functions(lg_obj):
  92         obj = getattr(lg_obj, f)
  93         if getattr(obj, "_cunumeric_implemented", False):
  94             lg_funcs.append(f)
  95     lg_funcs = set(lg_funcs)
  96 
  97     if exclude_mod:
  98         exclude_obj, _ = _import(exclude_mod, klass)
  99         exclude_funcs = _get_functions(exclude_obj)
  100         base_funcs -= exclude_funcs
  101         lg_funcs -= exclude_funcs
  102 
  103     buf = [
  104         header,
  105         "~" * len(header),
  106         "",
  107     ]
  108 
  109     buf += [
  110         ".. currentmodule:: cunumeric",
  111         "",
  112         ".. autosummary::",
  113         "   :toctree: generated/",
  114         "",
  115     ]
  116 
  117     buf += [
  118         ".. csv-table::",
  119         "   :header: NumPy, {}, single-GPU/CPU, multi-GPU/CPU".format(
  120             other_mod
  121         ),
  122         "",
  123     ]
  124     for f in sorted(base_funcs):
  125         base_cell = base_fmt.format(f)
  126         lg_cell = r"\-"
  127         single_gpu_cell = ""
  128         multi_gpu_cell = ""
  129         if f in lg_funcs:
  130             lg_cell = lg_fmt.format(f)
  131             obj = getattr(lg_obj, f)
  132             if obj.__doc__ is not None and "Single GPU" in obj.__doc__:
  133                 multi_gpu_cell = "No"
  134                 single_gpu_cell = "Yes"
  135             elif obj.__doc__ is not None and "Multiple GPUs" in obj.__doc__:
  136                 multi_gpu_cell = "Yes"
  137                 single_gpu_cell = "Yes"
  138             if getattr(base_obj, f) is getattr(lg_obj, f):
  139                 lg_cell = "{} (*alias of* {})".format(lg_cell, base_cell)
  140         line = "   {}, {}, {}, {}".format(
  141             base_cell, lg_cell, single_gpu_cell, multi_gpu_cell
  142         )
  143         buf.append(line)
  144 
  145     buf += [
  146         "",
  147         ".. Summary:",
  148         "   Number of NumPy functions: {}".format(len(base_funcs)),
  149         "   Number of functions covered by "
  150         f"{other_lib}: {len(lg_funcs & base_funcs)}",
  151     ]
  152     buf += [
  153         "",
  154     ]
  155     return buf
  156 
  157 
  158 def generate(other_lib):
  159     buf = []
  160     buf += [
  161         "NumPy vs cuNumeric APIs",
  162         "------------------------",
  163         "",
  164     ]
  165     buf += _section("Module-Level", "", other_lib)
  166     buf += _section("Ufuncs", "", other_lib, ufuncs=True)
  167     buf += _section("Multi-Dimensional Array", "", other_lib, klass="ndarray")
  168     buf += _section("Linear Algebra", ".linalg", other_lib)
  169     buf += _section("Discrete Fourier Transform", ".fft", other_lib)
  170     buf += _section("Random Sampling", ".random", other_lib)
  171 
  172     return "\n".join(buf)
  173 
  174 
  175 if __name__ == "__main__":
  176     print(generate("cunumeric"))
  177
  ```
  --- 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/docs/cunumeric/source/comparison/_comparison_generator.py b/docs/cunumeric/source/comparison/_comparison_generator.py
  --- a/docs/cunumeric/source/comparison/_comparison_generator.py
  +++ b/docs/cunumeric/source/comparison/_comparison_generator.py
  @@ -15,11 +15,8 @@
       "fastCopyAndTranspose",
       "get_array_wrap",
       "iterable",
  -    "loads",
  -    "mafromtxt",
       "max",
       "min",
  -    "ndfromtxt",
       "ndim",
       "product",
       "recfromcsv",
  @@ -32,6 +29,13 @@
       "test",
   ]
   
  +# these do not have valid intersphinx references
  +missing_numpy_refs = {
  +    "loads",
  +    "mafromtxt",
  +    "ndfromtxt",
  +}
  +
   
   def check_ufunc(obj, n):
       try:
  @@ -122,7 +126,10 @@
           "",
       ]
       for f in sorted(base_funcs):
  -        base_cell = base_fmt.format(f)
  +        if f not in missing_numpy_refs:
  +            base_cell = base_fmt.format(f)
  +        else:
  +            base_cell = f"``numpy.{f}``"
           lg_cell = r"\-"
           single_gpu_cell = ""
           multi_gpu_cell = ""
  
verification_info: {"golden_diff": "diff --git a/docs/cunumeric/source/comparison/_comparison_generator.py b/docs/cunumeric/source/comparison/_comparison_generator.py\n--- a/docs/cunumeric/source/comparison/_comparison_generator.py\n+++ b/docs/cunumeric/source/comparison/_comparison_generator.py\n@@ -15,11 +15,8 @@\n     \"fastCopyAndTranspose\",\n     \"get_array_wrap\",\n     \"iterable\",\n-    \"loads\",\n-    \"mafromtxt\",\n     \"max\",\n     \"min\",\n-    \"ndfromtxt\",\n     \"ndim\",\n     \"product\",\n     \"recfromcsv\",\n@@ -32,6 +29,13 @@\n     \"test\",\n ]\n \n+# these do not have valid intersphinx references\n+missing_numpy_refs = {\n+    \"loads\",\n+    \"mafromtxt\",\n+    \"ndfromtxt\",\n+}\n+\n \n def check_ufunc(obj, n):\n     try:\n@@ -122,7 +126,10 @@\n         \"\",\n     ]\n     for f in sorted(base_funcs):\n-        base_cell = base_fmt.format(f)\n+        if f not in missing_numpy_refs:\n+            base_cell = base_fmt.format(f)\n+        else:\n+            base_cell = f\"``numpy.{f}``\"\n         lg_cell = r\"\\-\"\n         single_gpu_cell = \"\"\n         multi_gpu_cell = \"\"\n", "issue": "Add some missing array methods \nA few numpy array methods were discovered to be missing:\r\n```\r\n                \"loads\",\r\n                \"mafromtxt\",\r\n                \"matmul\",\r\n                \"ndfromtxt\",\r\n```\r\n\r\nThese should be copied over as necessary, and the comparison blocklist updated. \r\n\r\nref: https://github.com/nv-legate/cunumeric/pull/224#discussion_r830657282\r\n\n", "before_files": [{"content": "import importlib\n\nimport numpy\n\nblocklist = [\n    \"abs\",\n    \"add_docstring\",\n    \"add_newdoc\",\n    \"add_newdoc_ufunc\",\n    \"alen\",\n    \"alltrue\",\n    \"bitwise_not\",\n    \"compare_chararrays\",\n    \"cumproduct\",\n    \"fastCopyAndTranspose\",\n    \"get_array_wrap\",\n    \"iterable\",\n    \"loads\",\n    \"mafromtxt\",\n    \"max\",\n    \"min\",\n    \"ndfromtxt\",\n    \"ndim\",\n    \"product\",\n    \"recfromcsv\",\n    \"recfromtxt\",\n    \"round\",\n    \"safe_eval\",\n    \"set_numeric_ops\",\n    \"size\",\n    \"sometrue\",\n    \"test\",\n]\n\n\ndef check_ufunc(obj, n):\n    try:\n        return isinstance(getattr(obj, n), numpy.ufunc)\n    except:  # noqa E722\n        return False\n\n\ndef _filter(obj, n, ufuncs=False):\n    is_ufunc = check_ufunc(obj, n)\n    if not ufuncs:\n        is_ufunc = not is_ufunc\n\n    try:\n        return (\n            n not in blocklist\n            and callable(getattr(obj, n))  # callable\n            and not isinstance(getattr(obj, n), type)  # not class\n            and n[0].islower()  # starts with lower char\n            and not n.startswith(\"__\")  # not special methods\n            and is_ufunc\n        )\n    except:  # noqa: E722\n        return False\n\n\ndef _get_functions(obj, ufuncs=False):\n    return set([n for n in dir(obj) if (_filter(obj, n, ufuncs))])\n\n\ndef _import(mod, klass):\n    try:\n        obj = importlib.import_module(mod)\n    except ModuleNotFoundError:\n        return None, None\n\n    if klass:\n        obj = getattr(obj, klass)\n        return obj, \":meth:`{}.{}.{{}}`\".format(mod, klass)\n    else:\n        # ufunc is not a function\n        return obj, \":obj:`{}.{{}}`\".format(mod)\n\n\ndef _section(\n    header, mod_ext, other_lib, klass=None, exclude_mod=None, ufuncs=False\n):\n    base_mod = \"numpy\" + mod_ext\n    other_mod = other_lib + mod_ext\n\n    base_funcs = []\n    base_obj, base_fmt = _import(base_mod, klass)\n    base_funcs = _get_functions(base_obj, ufuncs)\n    lg_obj, lg_fmt = _import(other_mod, klass)\n\n    lg_funcs = []\n    for f in _get_functions(lg_obj):\n        obj = getattr(lg_obj, f)\n        if getattr(obj, \"_cunumeric_implemented\", False):\n            lg_funcs.append(f)\n    lg_funcs = set(lg_funcs)\n\n    if exclude_mod:\n        exclude_obj, _ = _import(exclude_mod, klass)\n        exclude_funcs = _get_functions(exclude_obj)\n        base_funcs -= exclude_funcs\n        lg_funcs -= exclude_funcs\n\n    buf = [\n        header,\n        \"~\" * len(header),\n        \"\",\n    ]\n\n    buf += [\n        \".. currentmodule:: cunumeric\",\n        \"\",\n        \".. autosummary::\",\n        \"   :toctree: generated/\",\n        \"\",\n    ]\n\n    buf += [\n        \".. csv-table::\",\n        \"   :header: NumPy, {}, single-GPU/CPU, multi-GPU/CPU\".format(\n            other_mod\n        ),\n        \"\",\n    ]\n    for f in sorted(base_funcs):\n        base_cell = base_fmt.format(f)\n        lg_cell = r\"\\-\"\n        single_gpu_cell = \"\"\n        multi_gpu_cell = \"\"\n        if f in lg_funcs:\n            lg_cell = lg_fmt.format(f)\n            obj = getattr(lg_obj, f)\n            if obj.__doc__ is not None and \"Single GPU\" in obj.__doc__:\n                multi_gpu_cell = \"No\"\n                single_gpu_cell = \"Yes\"\n            elif obj.__doc__ is not None and \"Multiple GPUs\" in obj.__doc__:\n                multi_gpu_cell = \"Yes\"\n                single_gpu_cell = \"Yes\"\n            if getattr(base_obj, f) is getattr(lg_obj, f):\n                lg_cell = \"{} (*alias of* {})\".format(lg_cell, base_cell)\n        line = \"   {}, {}, {}, {}\".format(\n            base_cell, lg_cell, single_gpu_cell, multi_gpu_cell\n        )\n        buf.append(line)\n\n    buf += [\n        \"\",\n        \".. Summary:\",\n        \"   Number of NumPy functions: {}\".format(len(base_funcs)),\n        \"   Number of functions covered by \"\n        f\"{other_lib}: {len(lg_funcs & base_funcs)}\",\n    ]\n    buf += [\n        \"\",\n    ]\n    return buf\n\n\ndef generate(other_lib):\n    buf = []\n    buf += [\n        \"NumPy vs cuNumeric APIs\",\n        \"------------------------\",\n        \"\",\n    ]\n    buf += _section(\"Module-Level\", \"\", other_lib)\n    buf += _section(\"Ufuncs\", \"\", other_lib, ufuncs=True)\n    buf += _section(\"Multi-Dimensional Array\", \"\", other_lib, klass=\"ndarray\")\n    buf += _section(\"Linear Algebra\", \".linalg\", other_lib)\n    buf += _section(\"Discrete Fourier Transform\", \".fft\", other_lib)\n    buf += _section(\"Random Sampling\", \".random\", other_lib)\n\n    return \"\\n\".join(buf)\n\n\nif __name__ == \"__main__\":\n    print(generate(\"cunumeric\"))\n", "path": "docs/cunumeric/source/comparison/_comparison_generator.py"}], "after_files": [{"content": "import importlib\n\nimport numpy\n\nblocklist = [\n    \"abs\",\n    \"add_docstring\",\n    \"add_newdoc\",\n    \"add_newdoc_ufunc\",\n    \"alen\",\n    \"alltrue\",\n    \"bitwise_not\",\n    \"compare_chararrays\",\n    \"cumproduct\",\n    \"fastCopyAndTranspose\",\n    \"get_array_wrap\",\n    \"iterable\",\n    \"max\",\n    \"min\",\n    \"ndim\",\n    \"product\",\n    \"recfromcsv\",\n    \"recfromtxt\",\n    \"round\",\n    \"safe_eval\",\n    \"set_numeric_ops\",\n    \"size\",\n    \"sometrue\",\n    \"test\",\n]\n\n# these do not have valid intersphinx references\nmissing_numpy_refs = {\n    \"loads\",\n    \"mafromtxt\",\n    \"ndfromtxt\",\n}\n\n\ndef check_ufunc(obj, n):\n    try:\n        return isinstance(getattr(obj, n), numpy.ufunc)\n    except:  # noqa E722\n        return False\n\n\ndef _filter(obj, n, ufuncs=False):\n    is_ufunc = check_ufunc(obj, n)\n    if not ufuncs:\n        is_ufunc = not is_ufunc\n\n    try:\n        return (\n            n not in blocklist\n            and callable(getattr(obj, n))  # callable\n            and not isinstance(getattr(obj, n), type)  # not class\n            and n[0].islower()  # starts with lower char\n            and not n.startswith(\"__\")  # not special methods\n            and is_ufunc\n        )\n    except:  # noqa: E722\n        return False\n\n\ndef _get_functions(obj, ufuncs=False):\n    return set([n for n in dir(obj) if (_filter(obj, n, ufuncs))])\n\n\ndef _import(mod, klass):\n    try:\n        obj = importlib.import_module(mod)\n    except ModuleNotFoundError:\n        return None, None\n\n    if klass:\n        obj = getattr(obj, klass)\n        return obj, \":meth:`{}.{}.{{}}`\".format(mod, klass)\n    else:\n        # ufunc is not a function\n        return obj, \":obj:`{}.{{}}`\".format(mod)\n\n\ndef _section(\n    header, mod_ext, other_lib, klass=None, exclude_mod=None, ufuncs=False\n):\n    base_mod = \"numpy\" + mod_ext\n    other_mod = other_lib + mod_ext\n\n    base_funcs = []\n    base_obj, base_fmt = _import(base_mod, klass)\n    base_funcs = _get_functions(base_obj, ufuncs)\n    lg_obj, lg_fmt = _import(other_mod, klass)\n\n    lg_funcs = []\n    for f in _get_functions(lg_obj):\n        obj = getattr(lg_obj, f)\n        if getattr(obj, \"_cunumeric_implemented\", False):\n            lg_funcs.append(f)\n    lg_funcs = set(lg_funcs)\n\n    if exclude_mod:\n        exclude_obj, _ = _import(exclude_mod, klass)\n        exclude_funcs = _get_functions(exclude_obj)\n        base_funcs -= exclude_funcs\n        lg_funcs -= exclude_funcs\n\n    buf = [\n        header,\n        \"~\" * len(header),\n        \"\",\n    ]\n\n    buf += [\n        \".. currentmodule:: cunumeric\",\n        \"\",\n        \".. autosummary::\",\n        \"   :toctree: generated/\",\n        \"\",\n    ]\n\n    buf += [\n        \".. csv-table::\",\n        \"   :header: NumPy, {}, single-GPU/CPU, multi-GPU/CPU\".format(\n            other_mod\n        ),\n        \"\",\n    ]\n    for f in sorted(base_funcs):\n        if f not in missing_numpy_refs:\n            base_cell = base_fmt.format(f)\n        else:\n            base_cell = f\"``numpy.{f}``\"\n        lg_cell = r\"\\-\"\n        single_gpu_cell = \"\"\n        multi_gpu_cell = \"\"\n        if f in lg_funcs:\n            lg_cell = lg_fmt.format(f)\n            obj = getattr(lg_obj, f)\n            if obj.__doc__ is not None and \"Single GPU\" in obj.__doc__:\n                multi_gpu_cell = \"No\"\n                single_gpu_cell = \"Yes\"\n            elif obj.__doc__ is not None and \"Multiple GPUs\" in obj.__doc__:\n                multi_gpu_cell = \"Yes\"\n                single_gpu_cell = \"Yes\"\n            if getattr(base_obj, f) is getattr(lg_obj, f):\n                lg_cell = \"{} (*alias of* {})\".format(lg_cell, base_cell)\n        line = \"   {}, {}, {}, {}\".format(\n            base_cell, lg_cell, single_gpu_cell, multi_gpu_cell\n        )\n        buf.append(line)\n\n    buf += [\n        \"\",\n        \".. Summary:\",\n        \"   Number of NumPy functions: {}\".format(len(base_funcs)),\n        \"   Number of functions covered by \"\n        f\"{other_lib}: {len(lg_funcs & base_funcs)}\",\n    ]\n    buf += [\n        \"\",\n    ]\n    return buf\n\n\ndef generate(other_lib):\n    buf = []\n    buf += [\n        \"NumPy vs cuNumeric APIs\",\n        \"------------------------\",\n        \"\",\n    ]\n    buf += _section(\"Module-Level\", \"\", other_lib)\n    buf += _section(\"Ufuncs\", \"\", other_lib, ufuncs=True)\n    buf += _section(\"Multi-Dimensional Array\", \"\", other_lib, klass=\"ndarray\")\n    buf += _section(\"Linear Algebra\", \".linalg\", other_lib)\n    buf += _section(\"Discrete Fourier Transform\", \".fft\", other_lib)\n    buf += _section(\"Random Sampling\", \".random\", other_lib)\n\n    return \"\\n\".join(buf)\n\n\nif __name__ == \"__main__\":\n    print(generate(\"cunumeric\"))\n", "path": "docs/cunumeric/source/comparison/_comparison_generator.py"}]}