We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Inconsistency of type checking between SoftmaxCrossEntropy and Accuracy
We often evaluate training procedure with both `softmax_cross_entropy` and `accuracy`, as default `chainer.links.Classifier` does. But there are cases `softmax_cross_entorpy(x, t)` is accepted but `accuracy(x, t)` raises error because of its type checking (and vise versa). Replicated softmax is one of a such examples (e.g. `x.shape = (B, C, N)` and `t.shape = (B, N)`). We need to reshape them manually in such a case.

It is convenient for users that shape checking of the two be more consistent.


--- 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/evaluation/accuracy.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 Accuracy(function.Function):
9 
10     def __init__(self, ignore_label=None):
11         self.ignore_label = ignore_label
12 
13     def check_type_forward(self, in_types):
14         type_check.expect(in_types.size() == 2)
15         x_type, t_type = in_types
16 
17         type_check.expect(
18             x_type.dtype.kind == 'f',
19             x_type.ndim >= 2,
20             t_type.dtype == numpy.int32,
21             t_type.ndim == 1,
22             t_type.shape[0] == x_type.shape[0],
23         )
24         for i in range(2, x_type.ndim.eval()):
25             type_check.expect(x_type.shape[i] == 1)
26 
27     def forward(self, inputs):
28         xp = cuda.get_array_module(*inputs)
29         y, t = inputs
30         y = y.reshape(len(y), -1)  # flatten
31 
32         if self.ignore_label is not None:
33             mask = (t == self.ignore_label)
34             ignore_cnt = mask.sum()
35 
36             # will always be true when the true label is ignore_label
37             # TODO(henry0312)
38             #   If cupy.where returns indexes, we could make the code better.
39             #   Also, we would need Advanced Indexing.
40             pred = xp.where(mask, self.ignore_label, y.argmax(axis=1))
41             count = (pred == t).sum() - ignore_cnt
42             total = len(t) - ignore_cnt
43 
44             if total == 0:
45                 return xp.asarray(0.0, dtype=y.dtype),
46             else:
47                 return xp.asarray(float(count) / total, dtype=y.dtype),
48         else:
49             pred = y.argmax(axis=1)
50             return xp.asarray((pred == t).mean(dtype=y.dtype)),
51 
52 
53 def accuracy(y, t, ignore_label=None):
54     """Computes muticlass classification accuracy of the minibatch.
55 
56     Args:
57         y (Variable): Variable holding a matrix whose (i, j)-th element
58             indicates the score of the class j at the i-th example.
59         t (Variable): Variable holding an int32 vector of ground truth labels.
60         ignore_label (int or None): Skip calculating accuracy
61             if the ture label is ``ignore_label``.
62 
63     Returns:
64         Variable: A variable holding a scalar array of the accuracy.
65 
66     .. note:: This function is non-differentiable.
67 
68     """
69     return Accuracy(ignore_label=ignore_label)(y, t)
70
```
--- 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/evaluation/accuracy.py b/chainer/functions/evaluation/accuracy.py
--- a/chainer/functions/evaluation/accuracy.py
+++ b/chainer/functions/evaluation/accuracy.py
@@ -1,4 +1,5 @@
 import numpy
+import six
 
 from chainer import cuda
 from chainer import function
@@ -16,18 +17,21 @@
 
         type_check.expect(
             x_type.dtype.kind == 'f',
-            x_type.ndim >= 2,
-            t_type.dtype == numpy.int32,
-            t_type.ndim == 1,
-            t_type.shape[0] == x_type.shape[0],
+            t_type.dtype == numpy.int32
         )
-        for i in range(2, x_type.ndim.eval()):
+
+        t_ndim = t_type.ndim.eval()
+        type_check.expect(
+            x_type.ndim >= t_type.ndim,
+            x_type.shape[0] == t_type.shape[0],
+            x_type.shape[2: t_ndim + 1] == t_type.shape[1:]
+        )
+        for i in six.moves.range(t_ndim + 1, x_type.ndim.eval()):
             type_check.expect(x_type.shape[i] == 1)
 
     def forward(self, inputs):
         xp = cuda.get_array_module(*inputs)
         y, t = inputs
-        y = y.reshape(len(y), -1)  # flatten
 
         if self.ignore_label is not None:
             mask = (t == self.ignore_label)
@@ -37,16 +41,17 @@
             # TODO(henry0312)
             #   If cupy.where returns indexes, we could make the code better.
             #   Also, we would need Advanced Indexing.
-            pred = xp.where(mask, self.ignore_label, y.argmax(axis=1))
+            pred = xp.where(mask, self.ignore_label,
+                            y.argmax(axis=1).reshape(t.shape))
             count = (pred == t).sum() - ignore_cnt
-            total = len(t) - ignore_cnt
+            total = t.size - ignore_cnt
 
             if total == 0:
                 return xp.asarray(0.0, dtype=y.dtype),
             else:
                 return xp.asarray(float(count) / total, dtype=y.dtype),
         else:
-            pred = y.argmax(axis=1)
+            pred = y.argmax(axis=1).reshape(t.shape)
             return xp.asarray((pred == t).mean(dtype=y.dtype)),

{"golden_diff": "diff --git a/chainer/functions/evaluation/accuracy.py b/chainer/functions/evaluation/accuracy.py\n--- a/chainer/functions/evaluation/accuracy.py\n+++ b/chainer/functions/evaluation/accuracy.py\n@@ -1,4 +1,5 @@\n import numpy\n+import six\n \n from chainer import cuda\n from chainer import function\n@@ -16,18 +17,21 @@\n \n         type_check.expect(\n             x_type.dtype.kind == 'f',\n-            x_type.ndim >= 2,\n-            t_type.dtype == numpy.int32,\n-            t_type.ndim == 1,\n-            t_type.shape[0] == x_type.shape[0],\n+            t_type.dtype == numpy.int32\n         )\n-        for i in range(2, x_type.ndim.eval()):\n+\n+        t_ndim = t_type.ndim.eval()\n+        type_check.expect(\n+            x_type.ndim >= t_type.ndim,\n+            x_type.shape[0] == t_type.shape[0],\n+            x_type.shape[2: t_ndim + 1] == t_type.shape[1:]\n+        )\n+        for i in six.moves.range(t_ndim + 1, x_type.ndim.eval()):\n             type_check.expect(x_type.shape[i] == 1)\n \n     def forward(self, inputs):\n         xp = cuda.get_array_module(*inputs)\n         y, t = inputs\n-        y = y.reshape(len(y), -1)  # flatten\n \n         if self.ignore_label is not None:\n             mask = (t == self.ignore_label)\n@@ -37,16 +41,17 @@\n             # TODO(henry0312)\n             #   If cupy.where returns indexes, we could make the code better.\n             #   Also, we would need Advanced Indexing.\n-            pred = xp.where(mask, self.ignore_label, y.argmax(axis=1))\n+            pred = xp.where(mask, self.ignore_label,\n+                            y.argmax(axis=1).reshape(t.shape))\n             count = (pred == t).sum() - ignore_cnt\n-            total = len(t) - ignore_cnt\n+            total = t.size - ignore_cnt\n \n             if total == 0:\n                 return xp.asarray(0.0, dtype=y.dtype),\n             else:\n                 return xp.asarray(float(count) / total, dtype=y.dtype),\n         else:\n-            pred = y.argmax(axis=1)\n+            pred = y.argmax(axis=1).reshape(t.shape)\n             return xp.asarray((pred == t).mean(dtype=y.dtype)),\n", "issue": "Inconsistency of type checking between SoftmaxCrossEntropy and Accuracy\nWe often evaluate training procedure with both `softmax_cross_entropy` and `accuracy`, as default `chainer.links.Classifier` does. But there are cases `softmax_cross_entorpy(x, t)` is accepted but `accuracy(x, t)` raises error because of its type checking (and vise versa). Replicated softmax is one of a such examples (e.g. `x.shape = (B, C, N)` and `t.shape = (B, N)`). We need to reshape them manually in such a case.\n\nIt is convenient for users that shape checking of the two be more consistent.\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 Accuracy(function.Function):\n\n    def __init__(self, ignore_label=None):\n        self.ignore_label = ignore_label\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 2)\n        x_type, t_type = in_types\n\n        type_check.expect(\n            x_type.dtype.kind == 'f',\n            x_type.ndim >= 2,\n            t_type.dtype == numpy.int32,\n            t_type.ndim == 1,\n            t_type.shape[0] == x_type.shape[0],\n        )\n        for i in range(2, x_type.ndim.eval()):\n            type_check.expect(x_type.shape[i] == 1)\n\n    def forward(self, inputs):\n        xp = cuda.get_array_module(*inputs)\n        y, t = inputs\n        y = y.reshape(len(y), -1)  # flatten\n\n        if self.ignore_label is not None:\n            mask = (t == self.ignore_label)\n            ignore_cnt = mask.sum()\n\n            # will always be true when the true label is ignore_label\n            # TODO(henry0312)\n            #   If cupy.where returns indexes, we could make the code better.\n            #   Also, we would need Advanced Indexing.\n            pred = xp.where(mask, self.ignore_label, y.argmax(axis=1))\n            count = (pred == t).sum() - ignore_cnt\n            total = len(t) - ignore_cnt\n\n            if total == 0:\n                return xp.asarray(0.0, dtype=y.dtype),\n            else:\n                return xp.asarray(float(count) / total, dtype=y.dtype),\n        else:\n            pred = y.argmax(axis=1)\n            return xp.asarray((pred == t).mean(dtype=y.dtype)),\n\n\ndef accuracy(y, t, ignore_label=None):\n    \"\"\"Computes muticlass classification accuracy of the minibatch.\n\n    Args:\n        y (Variable): Variable holding a matrix whose (i, j)-th element\n            indicates the score of the class j at the i-th example.\n        t (Variable): Variable holding an int32 vector of ground truth labels.\n        ignore_label (int or None): Skip calculating accuracy\n            if the ture label is ``ignore_label``.\n\n    Returns:\n        Variable: A variable holding a scalar array of the accuracy.\n\n    .. note:: This function is non-differentiable.\n\n    \"\"\"\n    return Accuracy(ignore_label=ignore_label)(y, t)\n", "path": "chainer/functions/evaluation/accuracy.py"}], "after_files": [{"content": "import numpy\nimport six\n\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass Accuracy(function.Function):\n\n    def __init__(self, ignore_label=None):\n        self.ignore_label = ignore_label\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 2)\n        x_type, t_type = in_types\n\n        type_check.expect(\n            x_type.dtype.kind == 'f',\n            t_type.dtype == numpy.int32\n        )\n\n        t_ndim = t_type.ndim.eval()\n        type_check.expect(\n            x_type.ndim >= t_type.ndim,\n            x_type.shape[0] == t_type.shape[0],\n            x_type.shape[2: t_ndim + 1] == t_type.shape[1:]\n        )\n        for i in six.moves.range(t_ndim + 1, x_type.ndim.eval()):\n            type_check.expect(x_type.shape[i] == 1)\n\n    def forward(self, inputs):\n        xp = cuda.get_array_module(*inputs)\n        y, t = inputs\n\n        if self.ignore_label is not None:\n            mask = (t == self.ignore_label)\n            ignore_cnt = mask.sum()\n\n            # will always be true when the true label is ignore_label\n            # TODO(henry0312)\n            #   If cupy.where returns indexes, we could make the code better.\n            #   Also, we would need Advanced Indexing.\n            pred = xp.where(mask, self.ignore_label,\n                            y.argmax(axis=1).reshape(t.shape))\n            count = (pred == t).sum() - ignore_cnt\n            total = t.size - ignore_cnt\n\n            if total == 0:\n                return xp.asarray(0.0, dtype=y.dtype),\n            else:\n                return xp.asarray(float(count) / total, dtype=y.dtype),\n        else:\n            pred = y.argmax(axis=1).reshape(t.shape)\n            return xp.asarray((pred == t).mean(dtype=y.dtype)),\n\n\ndef accuracy(y, t, ignore_label=None):\n    \"\"\"Computes muticlass classification accuracy of the minibatch.\n\n    Args:\n        y (Variable): Variable holding a matrix whose (i, j)-th element\n            indicates the score of the class j at the i-th example.\n        t (Variable): Variable holding an int32 vector of ground truth labels.\n        ignore_label (int or None): Skip calculating accuracy\n            if the ture label is ``ignore_label``.\n\n    Returns:\n        Variable: A variable holding a scalar array of the accuracy.\n\n    .. note:: This function is non-differentiable.\n\n    \"\"\"\n    return Accuracy(ignore_label=ignore_label)(y, t)\n", "path": "chainer/functions/evaluation/accuracy.py"}]}