prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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)
  ```
  
  
  </issue>
  <code>
  [start of chainer/functions/loss/huber_loss.py]
  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 of chainer/functions/loss/huber_loss.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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
  
verification_info: {"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"}]}