We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
HuberLoss's backward() should not ignore gy
`HuberLoss`'s `backward()` ignores `gy`, so any computation after `HuberLoss` has no effect for gradients backpropagated by `HuberLoss`. I think such a behavior is not correct.

``` python
>>> x = chainer.Variable(np.zeros((1,1), dtype=np.float32))
>>> t = chainer.Variable(np.ones((1,1), dtype=np.float32))
>>> F.huber_loss(x, t, 1.0).backward()
>>> x.grad
array([[-1.]], dtype=float32)
```

``` python
>>> x = chainer.Variable(np.zeros((1,1), dtype=np.float32))
>>> t = chainer.Variable(np.ones((1,1), dtype=np.float32))
>>> (F.huber_loss(x, t, 1.0) * 0).backward()  # Multiply the loss by zero
>>> x.grad
array([[-1.]], dtype=float32)
```


--- 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/functions/loss/huber_loss.py`
Content:
```
1 import numpy
2 
3 from chainer import cuda
4 from chainer import function
5 from chainer.utils import type_check
6 
7 
8 class HuberLoss(function.Function):
9 
10     def __init__(self, delta):
11         self.delta = delta
12 
13     def check_type_forward(self, in_types):
14         type_check.expect(in_types.size() == 2)
15         type_check.expect(
16             in_types[0].dtype == numpy.float32,
17             in_types[1].dtype == numpy.float32,
18             in_types[0].shape == in_types[1].shape
19         )
20 
21     def forward(self, inputs):
22         xp = cuda.get_array_module(*inputs)
23         x0, x1 = inputs
24         self.diff = x0 - x1
25         y = xp.square(self.diff)
26         mask = y > (self.delta ** 2)
27         y -= mask * xp.square(abs(self.diff) - self.delta)
28         y *= 0.5
29         return y.sum(axis=1),
30 
31     def backward(self, inputs, gy):
32         xp = cuda.get_array_module(*inputs)
33         mask = xp.abs(self.diff) <= self.delta
34         gx = xp.where(mask, self.diff, self.delta * xp.sign(self.diff))
35         return gx, -gx
36 
37 
38 def huber_loss(x, t, delta):
39     """Loss function which is less sensitive to outliers in data than MSE.
40 
41         .. math::
42             a = x - t
43 
44         and
45 
46         .. math::
47             L_{\\delta}(a) = \\left \\{ \\begin{array}{cc}
48             \\frac{1}{2} a^2 & {\\rm if~|a| \\leq \\delta} \\\\
49             \\delta (|a| - \\frac{1}{2} \\delta) & {\\rm otherwise,}
50             \\end{array} \\right.
51 
52     Args:
53         x (~chainer.Variable): Input variable.
54             The shape of ``x`` should be (:math:`N`, :math:`K`).
55         t (~chainer.Variable): Target variable for regression.
56             The shape of ``t`` should be (:math:`N`, :math:`K`).
57         delta (float): Constant variable for huber loss function
58             as used in definition.
59 
60     Returns:
61         ~chainer.Variable: A variable object holding a scalar array of the
62             huber loss :math:`L_{\\delta}`.
63 
64     See:
65         `Huber loss - Wikipedia <https://en.wikipedia.org/wiki/Huber_loss>`_.
66 
67     """
68     return HuberLoss(delta=delta)(x, t)
69
```
--- 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/functions/loss/huber_loss.py b/chainer/functions/loss/huber_loss.py
--- a/chainer/functions/loss/huber_loss.py
+++ b/chainer/functions/loss/huber_loss.py
@@ -31,7 +31,8 @@
     def backward(self, inputs, gy):
         xp = cuda.get_array_module(*inputs)
         mask = xp.abs(self.diff) <= self.delta
-        gx = xp.where(mask, self.diff, self.delta * xp.sign(self.diff))
+        gx = gy[0].reshape(gy[0].shape + (1,) * (self.diff.ndim - 1)) * \
+            xp.where(mask, self.diff, self.delta * xp.sign(self.diff))
         return gx, -gx

{"golden_diff": "diff --git a/chainer/functions/loss/huber_loss.py b/chainer/functions/loss/huber_loss.py\n--- a/chainer/functions/loss/huber_loss.py\n+++ b/chainer/functions/loss/huber_loss.py\n@@ -31,7 +31,8 @@\n     def backward(self, inputs, gy):\n         xp = cuda.get_array_module(*inputs)\n         mask = xp.abs(self.diff) <= self.delta\n-        gx = xp.where(mask, self.diff, self.delta * xp.sign(self.diff))\n+        gx = gy[0].reshape(gy[0].shape + (1,) * (self.diff.ndim - 1)) * \\\n+            xp.where(mask, self.diff, self.delta * xp.sign(self.diff))\n         return gx, -gx\n", "issue": "HuberLoss's backward() should not ignore gy\n`HuberLoss`'s `backward()` ignores `gy`, so any computation after `HuberLoss` has no effect for gradients backpropagated by `HuberLoss`. I think such a behavior is not correct.\n\n``` python\n>>> x = chainer.Variable(np.zeros((1,1), dtype=np.float32))\n>>> t = chainer.Variable(np.ones((1,1), dtype=np.float32))\n>>> F.huber_loss(x, t, 1.0).backward()\n>>> x.grad\narray([[-1.]], dtype=float32)\n```\n\n``` python\n>>> x = chainer.Variable(np.zeros((1,1), dtype=np.float32))\n>>> t = chainer.Variable(np.ones((1,1), dtype=np.float32))\n>>> (F.huber_loss(x, t, 1.0) * 0).backward()  # Multiply the loss by zero\n>>> x.grad\narray([[-1.]], dtype=float32)\n```\n\n", "before_files": [{"content": "import numpy\n\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass HuberLoss(function.Function):\n\n    def __init__(self, delta):\n        self.delta = delta\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 2)\n        type_check.expect(\n            in_types[0].dtype == numpy.float32,\n            in_types[1].dtype == numpy.float32,\n            in_types[0].shape == in_types[1].shape\n        )\n\n    def forward(self, inputs):\n        xp = cuda.get_array_module(*inputs)\n        x0, x1 = inputs\n        self.diff = x0 - x1\n        y = xp.square(self.diff)\n        mask = y > (self.delta ** 2)\n        y -= mask * xp.square(abs(self.diff) - self.delta)\n        y *= 0.5\n        return y.sum(axis=1),\n\n    def backward(self, inputs, gy):\n        xp = cuda.get_array_module(*inputs)\n        mask = xp.abs(self.diff) <= self.delta\n        gx = xp.where(mask, self.diff, self.delta * xp.sign(self.diff))\n        return gx, -gx\n\n\ndef huber_loss(x, t, delta):\n    \"\"\"Loss function which is less sensitive to outliers in data than MSE.\n\n        .. math::\n            a = x - t\n\n        and\n\n        .. math::\n            L_{\\\\delta}(a) = \\\\left \\\\{ \\\\begin{array}{cc}\n            \\\\frac{1}{2} a^2 & {\\\\rm if~|a| \\\\leq \\\\delta} \\\\\\\\\n            \\\\delta (|a| - \\\\frac{1}{2} \\\\delta) & {\\\\rm otherwise,}\n            \\\\end{array} \\\\right.\n\n    Args:\n        x (~chainer.Variable): Input variable.\n            The shape of ``x`` should be (:math:`N`, :math:`K`).\n        t (~chainer.Variable): Target variable for regression.\n            The shape of ``t`` should be (:math:`N`, :math:`K`).\n        delta (float): Constant variable for huber loss function\n            as used in definition.\n\n    Returns:\n        ~chainer.Variable: A variable object holding a scalar array of the\n            huber loss :math:`L_{\\\\delta}`.\n\n    See:\n        `Huber loss - Wikipedia <https://en.wikipedia.org/wiki/Huber_loss>`_.\n\n    \"\"\"\n    return HuberLoss(delta=delta)(x, t)\n", "path": "chainer/functions/loss/huber_loss.py"}], "after_files": [{"content": "import numpy\n\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass HuberLoss(function.Function):\n\n    def __init__(self, delta):\n        self.delta = delta\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 2)\n        type_check.expect(\n            in_types[0].dtype == numpy.float32,\n            in_types[1].dtype == numpy.float32,\n            in_types[0].shape == in_types[1].shape\n        )\n\n    def forward(self, inputs):\n        xp = cuda.get_array_module(*inputs)\n        x0, x1 = inputs\n        self.diff = x0 - x1\n        y = xp.square(self.diff)\n        mask = y > (self.delta ** 2)\n        y -= mask * xp.square(abs(self.diff) - self.delta)\n        y *= 0.5\n        return y.sum(axis=1),\n\n    def backward(self, inputs, gy):\n        xp = cuda.get_array_module(*inputs)\n        mask = xp.abs(self.diff) <= self.delta\n        gx = gy[0].reshape(gy[0].shape + (1,) * (self.diff.ndim - 1)) * \\\n            xp.where(mask, self.diff, self.delta * xp.sign(self.diff))\n        return gx, -gx\n\n\ndef huber_loss(x, t, delta):\n    \"\"\"Loss function which is less sensitive to outliers in data than MSE.\n\n        .. math::\n            a = x - t\n\n        and\n\n        .. math::\n            L_{\\\\delta}(a) = \\\\left \\\\{ \\\\begin{array}{cc}\n            \\\\frac{1}{2} a^2 & {\\\\rm if~|a| \\\\leq \\\\delta} \\\\\\\\\n            \\\\delta (|a| - \\\\frac{1}{2} \\\\delta) & {\\\\rm otherwise,}\n            \\\\end{array} \\\\right.\n\n    Args:\n        x (~chainer.Variable): Input variable.\n            The shape of ``x`` should be (:math:`N`, :math:`K`).\n        t (~chainer.Variable): Target variable for regression.\n            The shape of ``t`` should be (:math:`N`, :math:`K`).\n        delta (float): Constant variable for huber loss function\n            as used in definition.\n\n    Returns:\n        ~chainer.Variable: A variable object holding a scalar array of the\n            huber loss :math:`L_{\\\\delta}`.\n\n    See:\n        `Huber loss - Wikipedia <https://en.wikipedia.org/wiki/Huber_loss>`_.\n\n    \"\"\"\n    return HuberLoss(delta=delta)(x, t)\n", "path": "chainer/functions/loss/huber_loss.py"}]}