We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
resuming issue of LinearShift
Same issue as #2680

```
import chainer
from chainer import iterators
from chainer import optimizers
from chainer import training
from chainer.training import extensions
from chainer import serializers


class DummyModel(chainer.Chain):

    def __call__(self, x):
        return x


def setup_trainer(iteration):
    model = DummyModel()
    optimizer = optimizers.SGD()
    optimizer.setup(model)

    iterator = iterators.SerialIterator([1, 2, 3], 1)

    updater = training.StandardUpdater(iterator, optimizer)
    trainer = training.Trainer(updater, (iteration, 'iteration'), out='.')

    trainer.extend(extensions.LogReport(trigger=(1, 'iteration')))
    trainer.extend(extensions.observe_lr(), trigger=(1, 'iteration'))
    trainer.extend(
        extensions.PrintReport(['iteration', 'lr']),
        trigger=(1, 'iteration'))

    trainer.extend(
        extensions.LinearShift('lr', (2, 1), (5, 15)),
        trigger=(1, 'iteration'))

    return trainer


trainer = setup_trainer(10)
trainer.run()
serializers.save_npz('tmp', trainer)
# iteration   lr
# 1           2
# 2           2
# 3           2
# 4           2
# 5           2
# 6           2
# 7           1.9
# 8           1.8
# 9           1.7
# 10          1.6

resumed_trainer = setup_trainer(20)
serializers.load_npz('tmp', resumed_trainer)
resumed_trainer.run()
# iteration   lr
# 1           2
# 2           2
# 3           2
# 4           2
# 5           2
# 6           2
# 7           1.9
# 8           1.8
# 9           1.7
# 10          1.6
# 11          1.4 (lr = 1.5 is skipped)
# 12          1.3
# 13          1.2
# 14          1.1
# 15          1
# 16          1
# 17          1
# 18          1
# 19          1
# 20          1
```

--- 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/training/extensions/linear_shift.py`
Content:
```
1 from __future__ import division
2 
3 from chainer.training import extension
4 
5 
6 class LinearShift(extension.Extension):
7 
8     """Trainer extension to change an optimizer attribute linearly.
9 
10     This extension changes an optimizer attribute from the first value to the
11     last value linearly within a specified duration. The typical use case is
12     warming up of the momentum coefficient.
13 
14     For example, suppose that this extension is called at every iteration, and
15     ``value_range == (x, y)`` and ``time_range == (i, j)``. Then, this
16     extension keeps the attribute to be ``x`` up to the ``i``-th iteration,
17     linearly shifts the value to ``y`` by the ``j``-th iteration, and then
18     keeps the value to be ``y`` after the ``j``-th iteration.
19 
20     This extension is also called before the training loop starts by default.
21 
22     Args:
23         attr (str): Name of the optimizer attribute to adjust.
24         value_range (tuple of float): The first and the last values of the
25             attribute.
26         time_range (tuple of ints): The first and last counts of calls in which
27             the attribute is adjusted.
28         optimizer (~chainer.Optimizer): Target optimizer object. If it is None,
29             the main optimizer of the trainer is used.
30 
31     """
32     invoke_before_training = True
33 
34     def __init__(self, attr, value_range, time_range, optimizer=None):
35         self._attr = attr
36         self._value_range = value_range
37         self._time_range = time_range
38         self._optimizer = optimizer
39         self._t = 0
40 
41     def __call__(self, trainer):
42         optimizer = self._optimizer or trainer.updater.get_optimizer('main')
43         t1, t2 = self._time_range
44         v1, v2 = self._value_range
45 
46         if self._t <= t1:
47             value = v1
48         elif self._t >= t2:
49             value = v2
50         else:
51             rate = (self._t - t1) / (t2 - t1)
52             value = v1 + rate * (v2 - v1)
53         setattr(optimizer, self._attr, value)
54 
55         self._t += 1
56 
57     def serialize(self, serializer):
58         self._t = serializer('_t', self._t)
59
```
--- 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/training/extensions/linear_shift.py b/chainer/training/extensions/linear_shift.py
--- a/chainer/training/extensions/linear_shift.py
+++ b/chainer/training/extensions/linear_shift.py
@@ -36,23 +36,34 @@
         self._value_range = value_range
         self._time_range = time_range
         self._optimizer = optimizer
-        self._t = 0
+        self._t = 1
+        self._before_training = True
 
     def __call__(self, trainer):
         optimizer = self._optimizer or trainer.updater.get_optimizer('main')
+
+        if self._before_training:
+            self._before_training = False
+            value = self._compute_value(self._t - 1)
+        else:
+            value = self._compute_value(self._t)
+            self._t += 1
+
+        setattr(optimizer, self._attr, value)
+
+    def serialize(self, serializer):
+        self._t = serializer('_t', self._t)
+
+    def _compute_value(self, t):
         t1, t2 = self._time_range
         v1, v2 = self._value_range
 
-        if self._t <= t1:
+        if t <= t1:
             value = v1
-        elif self._t >= t2:
+        elif t >= t2:
             value = v2
         else:
-            rate = (self._t - t1) / (t2 - t1)
+            rate = (t - t1) / (t2 - t1)
             value = v1 + rate * (v2 - v1)
-        setattr(optimizer, self._attr, value)
 
-        self._t += 1
-
-    def serialize(self, serializer):
-        self._t = serializer('_t', self._t)
+        return value

{"golden_diff": "diff --git a/chainer/training/extensions/linear_shift.py b/chainer/training/extensions/linear_shift.py\n--- a/chainer/training/extensions/linear_shift.py\n+++ b/chainer/training/extensions/linear_shift.py\n@@ -36,23 +36,34 @@\n         self._value_range = value_range\n         self._time_range = time_range\n         self._optimizer = optimizer\n-        self._t = 0\n+        self._t = 1\n+        self._before_training = True\n \n     def __call__(self, trainer):\n         optimizer = self._optimizer or trainer.updater.get_optimizer('main')\n+\n+        if self._before_training:\n+            self._before_training = False\n+            value = self._compute_value(self._t - 1)\n+        else:\n+            value = self._compute_value(self._t)\n+            self._t += 1\n+\n+        setattr(optimizer, self._attr, value)\n+\n+    def serialize(self, serializer):\n+        self._t = serializer('_t', self._t)\n+\n+    def _compute_value(self, t):\n         t1, t2 = self._time_range\n         v1, v2 = self._value_range\n \n-        if self._t <= t1:\n+        if t <= t1:\n             value = v1\n-        elif self._t >= t2:\n+        elif t >= t2:\n             value = v2\n         else:\n-            rate = (self._t - t1) / (t2 - t1)\n+            rate = (t - t1) / (t2 - t1)\n             value = v1 + rate * (v2 - v1)\n-        setattr(optimizer, self._attr, value)\n \n-        self._t += 1\n-\n-    def serialize(self, serializer):\n-        self._t = serializer('_t', self._t)\n+        return value\n", "issue": "resuming issue of LinearShift\nSame issue as #2680\r\n\r\n```\r\nimport chainer\r\nfrom chainer import iterators\r\nfrom chainer import optimizers\r\nfrom chainer import training\r\nfrom chainer.training import extensions\r\nfrom chainer import serializers\r\n\r\n\r\nclass DummyModel(chainer.Chain):\r\n\r\n    def __call__(self, x):\r\n        return x\r\n\r\n\r\ndef setup_trainer(iteration):\r\n    model = DummyModel()\r\n    optimizer = optimizers.SGD()\r\n    optimizer.setup(model)\r\n\r\n    iterator = iterators.SerialIterator([1, 2, 3], 1)\r\n\r\n    updater = training.StandardUpdater(iterator, optimizer)\r\n    trainer = training.Trainer(updater, (iteration, 'iteration'), out='.')\r\n\r\n    trainer.extend(extensions.LogReport(trigger=(1, 'iteration')))\r\n    trainer.extend(extensions.observe_lr(), trigger=(1, 'iteration'))\r\n    trainer.extend(\r\n        extensions.PrintReport(['iteration', 'lr']),\r\n        trigger=(1, 'iteration'))\r\n\r\n    trainer.extend(\r\n        extensions.LinearShift('lr', (2, 1), (5, 15)),\r\n        trigger=(1, 'iteration'))\r\n\r\n    return trainer\r\n\r\n\r\ntrainer = setup_trainer(10)\r\ntrainer.run()\r\nserializers.save_npz('tmp', trainer)\r\n# iteration   lr\r\n# 1           2\r\n# 2           2\r\n# 3           2\r\n# 4           2\r\n# 5           2\r\n# 6           2\r\n# 7           1.9\r\n# 8           1.8\r\n# 9           1.7\r\n# 10          1.6\r\n\r\nresumed_trainer = setup_trainer(20)\r\nserializers.load_npz('tmp', resumed_trainer)\r\nresumed_trainer.run()\r\n# iteration   lr\r\n# 1           2\r\n# 2           2\r\n# 3           2\r\n# 4           2\r\n# 5           2\r\n# 6           2\r\n# 7           1.9\r\n# 8           1.8\r\n# 9           1.7\r\n# 10          1.6\r\n# 11          1.4 (lr = 1.5 is skipped)\r\n# 12          1.3\r\n# 13          1.2\r\n# 14          1.1\r\n# 15          1\r\n# 16          1\r\n# 17          1\r\n# 18          1\r\n# 19          1\r\n# 20          1\r\n```\n", "before_files": [{"content": "from __future__ import division\n\nfrom chainer.training import extension\n\n\nclass LinearShift(extension.Extension):\n\n    \"\"\"Trainer extension to change an optimizer attribute linearly.\n\n    This extension changes an optimizer attribute from the first value to the\n    last value linearly within a specified duration. The typical use case is\n    warming up of the momentum coefficient.\n\n    For example, suppose that this extension is called at every iteration, and\n    ``value_range == (x, y)`` and ``time_range == (i, j)``. Then, this\n    extension keeps the attribute to be ``x`` up to the ``i``-th iteration,\n    linearly shifts the value to ``y`` by the ``j``-th iteration, and then\n    keeps the value to be ``y`` after the ``j``-th iteration.\n\n    This extension is also called before the training loop starts by default.\n\n    Args:\n        attr (str): Name of the optimizer attribute to adjust.\n        value_range (tuple of float): The first and the last values of the\n            attribute.\n        time_range (tuple of ints): The first and last counts of calls in which\n            the attribute is adjusted.\n        optimizer (~chainer.Optimizer): Target optimizer object. If it is None,\n            the main optimizer of the trainer is used.\n\n    \"\"\"\n    invoke_before_training = True\n\n    def __init__(self, attr, value_range, time_range, optimizer=None):\n        self._attr = attr\n        self._value_range = value_range\n        self._time_range = time_range\n        self._optimizer = optimizer\n        self._t = 0\n\n    def __call__(self, trainer):\n        optimizer = self._optimizer or trainer.updater.get_optimizer('main')\n        t1, t2 = self._time_range\n        v1, v2 = self._value_range\n\n        if self._t <= t1:\n            value = v1\n        elif self._t >= t2:\n            value = v2\n        else:\n            rate = (self._t - t1) / (t2 - t1)\n            value = v1 + rate * (v2 - v1)\n        setattr(optimizer, self._attr, value)\n\n        self._t += 1\n\n    def serialize(self, serializer):\n        self._t = serializer('_t', self._t)\n", "path": "chainer/training/extensions/linear_shift.py"}], "after_files": [{"content": "from __future__ import division\n\nfrom chainer.training import extension\n\n\nclass LinearShift(extension.Extension):\n\n    \"\"\"Trainer extension to change an optimizer attribute linearly.\n\n    This extension changes an optimizer attribute from the first value to the\n    last value linearly within a specified duration. The typical use case is\n    warming up of the momentum coefficient.\n\n    For example, suppose that this extension is called at every iteration, and\n    ``value_range == (x, y)`` and ``time_range == (i, j)``. Then, this\n    extension keeps the attribute to be ``x`` up to the ``i``-th iteration,\n    linearly shifts the value to ``y`` by the ``j``-th iteration, and then\n    keeps the value to be ``y`` after the ``j``-th iteration.\n\n    This extension is also called before the training loop starts by default.\n\n    Args:\n        attr (str): Name of the optimizer attribute to adjust.\n        value_range (tuple of float): The first and the last values of the\n            attribute.\n        time_range (tuple of ints): The first and last counts of calls in which\n            the attribute is adjusted.\n        optimizer (~chainer.Optimizer): Target optimizer object. If it is None,\n            the main optimizer of the trainer is used.\n\n    \"\"\"\n    invoke_before_training = True\n\n    def __init__(self, attr, value_range, time_range, optimizer=None):\n        self._attr = attr\n        self._value_range = value_range\n        self._time_range = time_range\n        self._optimizer = optimizer\n        self._t = 1\n        self._before_training = True\n\n    def __call__(self, trainer):\n        optimizer = self._optimizer or trainer.updater.get_optimizer('main')\n\n        if self._before_training:\n            self._before_training = False\n            value = self._compute_value(self._t - 1)\n        else:\n            value = self._compute_value(self._t)\n            self._t += 1\n\n        setattr(optimizer, self._attr, value)\n\n    def serialize(self, serializer):\n        self._t = serializer('_t', self._t)\n\n    def _compute_value(self, t):\n        t1, t2 = self._time_range\n        v1, v2 = self._value_range\n\n        if t <= t1:\n            value = v1\n        elif t >= t2:\n            value = v2\n        else:\n            rate = (t - t1) / (t2 - t1)\n            value = v1 + rate * (v2 - v1)\n\n        return value\n", "path": "chainer/training/extensions/linear_shift.py"}]}