We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
A confusing error occurs when GPU and CPU arrays are mixed on `numerical_grad`
In this case, unreadable error occurs. We nee to check if arrays are not mixed.


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `chainer/gradient_check.py`
Content:
```
1 import numpy
2 import six
3 
4 from chainer import cuda
5 
6 
7 def numerical_grad_cpu(f, inputs, grad_outputs, eps=1e-3):
8     grads = tuple(numpy.zeros_like(x) for x in inputs)
9     for x, gx in zip(inputs, grads):
10         flat_x = x.ravel()
11         flat_gx = gx.ravel()
12         for i in six.moves.range(flat_x.size):
13             orig = flat_x[i]
14             flat_x[i] = orig + eps
15             ys1 = f()
16             flat_x[i] = orig - eps
17             ys2 = f()
18             flat_x[i] = orig
19 
20             for y1, y2, gy in zip(ys1, ys2, grad_outputs):
21                 if gy is not None:
22                     dot = float(sum(((y1 - y2) * gy).ravel()))
23                     flat_gx[i] += dot / (2 * eps)
24 
25     return grads
26 
27 
28 def numerical_grad_gpu(f, inputs, grad_outputs, eps=1e-3):
29     grads = tuple(cuda.zeros_like(x) for x in inputs)
30     for x, gx in zip(inputs, grads):
31         x = x.ravel()
32         gx = gx.ravel()
33         x_cpu = x.get()
34         gx_cpu = gx.get()
35         for i in six.moves.range(x_cpu.size):
36             orig = x_cpu[i]
37             x_cpu[i] = orig + eps
38             x.set(x_cpu)
39             ys1 = f()
40             x_cpu[i] = orig - eps
41             x.set(x_cpu)
42             ys2 = f()
43             x_cpu[i] = orig
44             x.set(x_cpu)
45 
46             for y1, y2, gy in zip(ys1, ys2, grad_outputs):
47                 if gy is not None:
48                     dot = sum(((y1 - y2) * gy).ravel()).get()
49                     gx_cpu[i] += dot / (2 * eps)
50         gx.set(gx_cpu)
51 
52     return grads
53 
54 
55 def numerical_grad(f, inputs, grad_outputs, eps=1e-3):
56     """Computes numerical gradient by finite differences.
57 
58     This function is used to implement gradient check. For usage example, see
59     unit tests of :mod:`chainer.functions`.
60 
61     Args:
62         f (function): Python function with no arguments that runs forward
63             computation and returns the result.
64         inputs (tuple of arrays): Tuple of arrays that should be treated as
65             inputs. Each element of them is slightly modified to realize
66             numerical gradient by finite differences.
67         grad_outputs (tuple of arrays): Tuple of arrays that are treated as
68             output gradients.
69         eps (float): Epsilon value of finite differences.
70 
71     Returns:
72         tuple: Numerical gradient arrays corresponding to ``inputs``.
73 
74     """
75     assert eps > 0
76     if any(isinstance(x, cuda.GPUArray) for x in inputs):
77         return numerical_grad_gpu(f, inputs, grad_outputs, eps)
78     return numerical_grad_cpu(f, inputs, grad_outputs, eps)
79 
80 
81 def assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):
82     """Asserts if some corresponding element of x and y differs too much.
83 
84     This function can handle both CPU and GPU arrays simultaneously.
85 
86     Args:
87         x: Left-hand-side array.
88         y: Right-hand-side array.
89         atol (float): Absolute tolerance.
90         rtol (float): Relative tolerance.
91         verbose (bool): If True, it outputs verbose messages on error.
92 
93     """
94     x = cuda.to_cpu(x)
95     y = cuda.to_cpu(y)
96     try:
97         numpy.testing.assert_allclose(
98             x, y, atol=atol, rtol=rtol, verbose=verbose)
99     except Exception:
100         print('error:', numpy.abs(x - y).max())
101         raise
102
```
--- 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/chainer/gradient_check.py b/chainer/gradient_check.py
--- a/chainer/gradient_check.py
+++ b/chainer/gradient_check.py
@@ -73,9 +73,18 @@
 
     """
     assert eps > 0
-    if any(isinstance(x, cuda.GPUArray) for x in inputs):
+    inputs = tuple(inputs)
+    grad_outputs = tuple(grad_outputs)
+    gpu = any(isinstance(x, cuda.GPUArray) for x in inputs + grad_outputs)
+
+    cpu = any(isinstance(x, numpy.ndarray) for x in inputs + grad_outputs)
+
+    if gpu and cpu:
+        raise RuntimeError('Do not mix GPU and CPU arrays in `numerical_grad`')
+    elif gpu:
         return numerical_grad_gpu(f, inputs, grad_outputs, eps)
-    return numerical_grad_cpu(f, inputs, grad_outputs, eps)
+    else:
+        return numerical_grad_cpu(f, inputs, grad_outputs, eps)
 
 
 def assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):

{"golden_diff": "diff --git a/chainer/gradient_check.py b/chainer/gradient_check.py\n--- a/chainer/gradient_check.py\n+++ b/chainer/gradient_check.py\n@@ -73,9 +73,18 @@\n \n     \"\"\"\n     assert eps > 0\n-    if any(isinstance(x, cuda.GPUArray) for x in inputs):\n+    inputs = tuple(inputs)\n+    grad_outputs = tuple(grad_outputs)\n+    gpu = any(isinstance(x, cuda.GPUArray) for x in inputs + grad_outputs)\n+\n+    cpu = any(isinstance(x, numpy.ndarray) for x in inputs + grad_outputs)\n+\n+    if gpu and cpu:\n+        raise RuntimeError('Do not mix GPU and CPU arrays in `numerical_grad`')\n+    elif gpu:\n         return numerical_grad_gpu(f, inputs, grad_outputs, eps)\n-    return numerical_grad_cpu(f, inputs, grad_outputs, eps)\n+    else:\n+        return numerical_grad_cpu(f, inputs, grad_outputs, eps)\n \n \n def assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):\n", "issue": "A confusing error occurs when GPU and CPU arrays are mixed on `numerical_grad`\nIn this case, unreadable error occurs. We nee to check if arrays are not mixed.\n\n", "before_files": [{"content": "import numpy\nimport six\n\nfrom chainer import cuda\n\n\ndef numerical_grad_cpu(f, inputs, grad_outputs, eps=1e-3):\n    grads = tuple(numpy.zeros_like(x) for x in inputs)\n    for x, gx in zip(inputs, grads):\n        flat_x = x.ravel()\n        flat_gx = gx.ravel()\n        for i in six.moves.range(flat_x.size):\n            orig = flat_x[i]\n            flat_x[i] = orig + eps\n            ys1 = f()\n            flat_x[i] = orig - eps\n            ys2 = f()\n            flat_x[i] = orig\n\n            for y1, y2, gy in zip(ys1, ys2, grad_outputs):\n                if gy is not None:\n                    dot = float(sum(((y1 - y2) * gy).ravel()))\n                    flat_gx[i] += dot / (2 * eps)\n\n    return grads\n\n\ndef numerical_grad_gpu(f, inputs, grad_outputs, eps=1e-3):\n    grads = tuple(cuda.zeros_like(x) for x in inputs)\n    for x, gx in zip(inputs, grads):\n        x = x.ravel()\n        gx = gx.ravel()\n        x_cpu = x.get()\n        gx_cpu = gx.get()\n        for i in six.moves.range(x_cpu.size):\n            orig = x_cpu[i]\n            x_cpu[i] = orig + eps\n            x.set(x_cpu)\n            ys1 = f()\n            x_cpu[i] = orig - eps\n            x.set(x_cpu)\n            ys2 = f()\n            x_cpu[i] = orig\n            x.set(x_cpu)\n\n            for y1, y2, gy in zip(ys1, ys2, grad_outputs):\n                if gy is not None:\n                    dot = sum(((y1 - y2) * gy).ravel()).get()\n                    gx_cpu[i] += dot / (2 * eps)\n        gx.set(gx_cpu)\n\n    return grads\n\n\ndef numerical_grad(f, inputs, grad_outputs, eps=1e-3):\n    \"\"\"Computes numerical gradient by finite differences.\n\n    This function is used to implement gradient check. For usage example, see\n    unit tests of :mod:`chainer.functions`.\n\n    Args:\n        f (function): Python function with no arguments that runs forward\n            computation and returns the result.\n        inputs (tuple of arrays): Tuple of arrays that should be treated as\n            inputs. Each element of them is slightly modified to realize\n            numerical gradient by finite differences.\n        grad_outputs (tuple of arrays): Tuple of arrays that are treated as\n            output gradients.\n        eps (float): Epsilon value of finite differences.\n\n    Returns:\n        tuple: Numerical gradient arrays corresponding to ``inputs``.\n\n    \"\"\"\n    assert eps > 0\n    if any(isinstance(x, cuda.GPUArray) for x in inputs):\n        return numerical_grad_gpu(f, inputs, grad_outputs, eps)\n    return numerical_grad_cpu(f, inputs, grad_outputs, eps)\n\n\ndef assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):\n    \"\"\"Asserts if some corresponding element of x and y differs too much.\n\n    This function can handle both CPU and GPU arrays simultaneously.\n\n    Args:\n        x: Left-hand-side array.\n        y: Right-hand-side array.\n        atol (float): Absolute tolerance.\n        rtol (float): Relative tolerance.\n        verbose (bool): If True, it outputs verbose messages on error.\n\n    \"\"\"\n    x = cuda.to_cpu(x)\n    y = cuda.to_cpu(y)\n    try:\n        numpy.testing.assert_allclose(\n            x, y, atol=atol, rtol=rtol, verbose=verbose)\n    except Exception:\n        print('error:', numpy.abs(x - y).max())\n        raise\n", "path": "chainer/gradient_check.py"}], "after_files": [{"content": "import numpy\nimport six\n\nfrom chainer import cuda\n\n\ndef numerical_grad_cpu(f, inputs, grad_outputs, eps=1e-3):\n    grads = tuple(numpy.zeros_like(x) for x in inputs)\n    for x, gx in zip(inputs, grads):\n        flat_x = x.ravel()\n        flat_gx = gx.ravel()\n        for i in six.moves.range(flat_x.size):\n            orig = flat_x[i]\n            flat_x[i] = orig + eps\n            ys1 = f()\n            flat_x[i] = orig - eps\n            ys2 = f()\n            flat_x[i] = orig\n\n            for y1, y2, gy in zip(ys1, ys2, grad_outputs):\n                if gy is not None:\n                    dot = float(sum(((y1 - y2) * gy).ravel()))\n                    flat_gx[i] += dot / (2 * eps)\n\n    return grads\n\n\ndef numerical_grad_gpu(f, inputs, grad_outputs, eps=1e-3):\n    grads = tuple(cuda.zeros_like(x) for x in inputs)\n    for x, gx in zip(inputs, grads):\n        x = x.ravel()\n        gx = gx.ravel()\n        x_cpu = x.get()\n        gx_cpu = gx.get()\n        for i in six.moves.range(x_cpu.size):\n            orig = x_cpu[i]\n            x_cpu[i] = orig + eps\n            x.set(x_cpu)\n            ys1 = f()\n            x_cpu[i] = orig - eps\n            x.set(x_cpu)\n            ys2 = f()\n            x_cpu[i] = orig\n            x.set(x_cpu)\n\n            for y1, y2, gy in zip(ys1, ys2, grad_outputs):\n                if gy is not None:\n                    dot = sum(((y1 - y2) * gy).ravel()).get()\n                    gx_cpu[i] += dot / (2 * eps)\n        gx.set(gx_cpu)\n\n    return grads\n\n\ndef numerical_grad(f, inputs, grad_outputs, eps=1e-3):\n    \"\"\"Computes numerical gradient by finite differences.\n\n    This function is used to implement gradient check. For usage example, see\n    unit tests of :mod:`chainer.functions`.\n\n    Args:\n        f (function): Python function with no arguments that runs forward\n            computation and returns the result.\n        inputs (tuple of arrays): Tuple of arrays that should be treated as\n            inputs. Each element of them is slightly modified to realize\n            numerical gradient by finite differences.\n        grad_outputs (tuple of arrays): Tuple of arrays that are treated as\n            output gradients.\n        eps (float): Epsilon value of finite differences.\n\n    Returns:\n        tuple: Numerical gradient arrays corresponding to ``inputs``.\n\n    \"\"\"\n    assert eps > 0\n    inputs = tuple(inputs)\n    grad_outputs = tuple(grad_outputs)\n    gpu = any(isinstance(x, cuda.GPUArray) for x in inputs + grad_outputs)\n\n    cpu = any(isinstance(x, numpy.ndarray) for x in inputs + grad_outputs)\n\n    if gpu and cpu:\n        raise RuntimeError('Do not mix GPU and CPU arrays in `numerical_grad`')\n    elif gpu:\n        return numerical_grad_gpu(f, inputs, grad_outputs, eps)\n    else:\n        return numerical_grad_cpu(f, inputs, grad_outputs, eps)\n\n\ndef assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):\n    \"\"\"Asserts if some corresponding element of x and y differs too much.\n\n    This function can handle both CPU and GPU arrays simultaneously.\n\n    Args:\n        x: Left-hand-side array.\n        y: Right-hand-side array.\n        atol (float): Absolute tolerance.\n        rtol (float): Relative tolerance.\n        verbose (bool): If True, it outputs verbose messages on error.\n\n    \"\"\"\n    x = cuda.to_cpu(x)\n    y = cuda.to_cpu(y)\n    try:\n        numpy.testing.assert_allclose(\n            x, y, atol=atol, rtol=rtol, verbose=verbose)\n    except Exception:\n        print('error:', numpy.abs(x - y).max())\n        raise\n", "path": "chainer/gradient_check.py"}]}