We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Replicability in Neural networks and Random forests
Follow up from #3715: Neural networks and Random forests should have a checkbox `Replicable training` or something like this, which would decide whether random seed is fixed (to 0) or "random".

In Neural networks: add the check box.

In Random forest: remove the spin box.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `Orange/widgets/model/owrandomforest.py`
Content:
```
1 from AnyQt.QtCore import Qt
2 
3 from Orange.data import Table
4 from Orange.modelling import RandomForestLearner
5 from Orange.widgets import settings, gui
6 from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
7 from Orange.widgets.utils.widgetpreview import WidgetPreview
8 from Orange.widgets.widget import Msg
9 
10 
11 class OWRandomForest(OWBaseLearner):
12     name = "Random Forest"
13     description = "Predict using an ensemble of decision trees."
14     icon = "icons/RandomForest.svg"
15     replaces = [
16         "Orange.widgets.classify.owrandomforest.OWRandomForest",
17         "Orange.widgets.regression.owrandomforestregression.OWRandomForestRegression",
18     ]
19     priority = 40
20     keywords = []
21 
22     LEARNER = RandomForestLearner
23 
24     n_estimators = settings.Setting(10)
25     max_features = settings.Setting(5)
26     use_max_features = settings.Setting(False)
27     random_state = settings.Setting(0)
28     use_random_state = settings.Setting(False)
29     max_depth = settings.Setting(3)
30     use_max_depth = settings.Setting(False)
31     min_samples_split = settings.Setting(5)
32     use_min_samples_split = settings.Setting(True)
33     index_output = settings.Setting(0)
34 
35     class Error(OWBaseLearner.Error):
36         not_enough_features = Msg("Insufficient number of attributes ({})")
37 
38     def add_main_layout(self):
39         box = gui.vBox(self.controlArea, 'Basic Properties')
40         self.n_estimators_spin = gui.spin(
41             box, self, "n_estimators", minv=1, maxv=10000, controlWidth=80,
42             alignment=Qt.AlignRight, label="Number of trees: ",
43             callback=self.settings_changed)
44         self.max_features_spin = gui.spin(
45             box, self, "max_features", 2, 50, controlWidth=80,
46             label="Number of attributes considered at each split: ",
47             callback=self.settings_changed, checked="use_max_features",
48             checkCallback=self.settings_changed, alignment=Qt.AlignRight,)
49         self.random_state_spin = gui.spin(
50             box, self, "random_state", 0, 2 ** 31 - 1, controlWidth=80,
51             label="Fixed seed for random generator: ", alignment=Qt.AlignRight,
52             callback=self.settings_changed, checked="use_random_state",
53             checkCallback=self.settings_changed)
54 
55         box = gui.vBox(self.controlArea, "Growth Control")
56         self.max_depth_spin = gui.spin(
57             box, self, "max_depth", 1, 50, controlWidth=80,
58             label="Limit depth of individual trees: ", alignment=Qt.AlignRight,
59             callback=self.settings_changed, checked="use_max_depth",
60             checkCallback=self.settings_changed)
61         self.min_samples_split_spin = gui.spin(
62             box, self, "min_samples_split", 2, 1000, controlWidth=80,
63             label="Do not split subsets smaller than: ",
64             callback=self.settings_changed, checked="use_min_samples_split",
65             checkCallback=self.settings_changed, alignment=Qt.AlignRight)
66 
67     def create_learner(self):
68         common_args = {"n_estimators": self.n_estimators}
69         if self.use_max_features:
70             common_args["max_features"] = self.max_features
71         if self.use_random_state:
72             common_args["random_state"] = self.random_state
73         if self.use_max_depth:
74             common_args["max_depth"] = self.max_depth
75         if self.use_min_samples_split:
76             common_args["min_samples_split"] = self.min_samples_split
77 
78         return self.LEARNER(preprocessors=self.preprocessors, **common_args)
79 
80     def check_data(self):
81         self.Error.not_enough_features.clear()
82         if super().check_data():
83             n_features = len(self.data.domain.attributes)
84             if self.use_max_features and self.max_features > n_features:
85                 self.Error.not_enough_features(n_features)
86                 self.valid_data = False
87         return self.valid_data
88 
89     def get_learner_parameters(self):
90         """Called by send report to list the parameters of the learner."""
91         return (
92             ("Number of trees", self.n_estimators),
93             ("Maximal number of considered features",
94              self.max_features if self.use_max_features else "unlimited"),
95             ("Fixed random seed", self.use_random_state and self.random_state),
96             ("Maximal tree depth",
97              self.max_depth if self.use_max_depth else "unlimited"),
98             ("Stop splitting nodes with maximum instances",
99              self.min_samples_split if self.use_min_samples_split else "unlimited")
100         )
101 
102 
103 if __name__ == "__main__":  # pragma: no cover
104     WidgetPreview(OWRandomForest).run(Table("iris"))
105
```
--- 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/Orange/widgets/model/owrandomforest.py b/Orange/widgets/model/owrandomforest.py
--- a/Orange/widgets/model/owrandomforest.py
+++ b/Orange/widgets/model/owrandomforest.py
@@ -24,7 +24,6 @@
     n_estimators = settings.Setting(10)
     max_features = settings.Setting(5)
     use_max_features = settings.Setting(False)
-    random_state = settings.Setting(0)
     use_random_state = settings.Setting(False)
     max_depth = settings.Setting(3)
     use_max_depth = settings.Setting(False)
@@ -46,11 +45,9 @@
             label="Number of attributes considered at each split: ",
             callback=self.settings_changed, checked="use_max_features",
             checkCallback=self.settings_changed, alignment=Qt.AlignRight,)
-        self.random_state_spin = gui.spin(
-            box, self, "random_state", 0, 2 ** 31 - 1, controlWidth=80,
-            label="Fixed seed for random generator: ", alignment=Qt.AlignRight,
-            callback=self.settings_changed, checked="use_random_state",
-            checkCallback=self.settings_changed)
+        self.random_state = gui.checkBox(
+            box, self, "use_random_state", label="Replicable training",
+            callback=self.settings_changed)
 
         box = gui.vBox(self.controlArea, "Growth Control")
         self.max_depth_spin = gui.spin(
@@ -69,7 +66,7 @@
         if self.use_max_features:
             common_args["max_features"] = self.max_features
         if self.use_random_state:
-            common_args["random_state"] = self.random_state
+            common_args["random_state"] = 0
         if self.use_max_depth:
             common_args["max_depth"] = self.max_depth
         if self.use_min_samples_split:
@@ -92,7 +89,7 @@
             ("Number of trees", self.n_estimators),
             ("Maximal number of considered features",
              self.max_features if self.use_max_features else "unlimited"),
-            ("Fixed random seed", self.use_random_state and self.random_state),
+            ("Replicable training", ["No", "Yes"][self.use_random_state]),
             ("Maximal tree depth",
              self.max_depth if self.use_max_depth else "unlimited"),
             ("Stop splitting nodes with maximum instances",

{"golden_diff": "diff --git a/Orange/widgets/model/owrandomforest.py b/Orange/widgets/model/owrandomforest.py\n--- a/Orange/widgets/model/owrandomforest.py\n+++ b/Orange/widgets/model/owrandomforest.py\n@@ -24,7 +24,6 @@\n     n_estimators = settings.Setting(10)\n     max_features = settings.Setting(5)\n     use_max_features = settings.Setting(False)\n-    random_state = settings.Setting(0)\n     use_random_state = settings.Setting(False)\n     max_depth = settings.Setting(3)\n     use_max_depth = settings.Setting(False)\n@@ -46,11 +45,9 @@\n             label=\"Number of attributes considered at each split: \",\n             callback=self.settings_changed, checked=\"use_max_features\",\n             checkCallback=self.settings_changed, alignment=Qt.AlignRight,)\n-        self.random_state_spin = gui.spin(\n-            box, self, \"random_state\", 0, 2 ** 31 - 1, controlWidth=80,\n-            label=\"Fixed seed for random generator: \", alignment=Qt.AlignRight,\n-            callback=self.settings_changed, checked=\"use_random_state\",\n-            checkCallback=self.settings_changed)\n+        self.random_state = gui.checkBox(\n+            box, self, \"use_random_state\", label=\"Replicable training\",\n+            callback=self.settings_changed)\n \n         box = gui.vBox(self.controlArea, \"Growth Control\")\n         self.max_depth_spin = gui.spin(\n@@ -69,7 +66,7 @@\n         if self.use_max_features:\n             common_args[\"max_features\"] = self.max_features\n         if self.use_random_state:\n-            common_args[\"random_state\"] = self.random_state\n+            common_args[\"random_state\"] = 0\n         if self.use_max_depth:\n             common_args[\"max_depth\"] = self.max_depth\n         if self.use_min_samples_split:\n@@ -92,7 +89,7 @@\n             (\"Number of trees\", self.n_estimators),\n             (\"Maximal number of considered features\",\n              self.max_features if self.use_max_features else \"unlimited\"),\n-            (\"Fixed random seed\", self.use_random_state and self.random_state),\n+            (\"Replicable training\", [\"No\", \"Yes\"][self.use_random_state]),\n             (\"Maximal tree depth\",\n              self.max_depth if self.use_max_depth else \"unlimited\"),\n             (\"Stop splitting nodes with maximum instances\",\n", "issue": "Replicability in Neural networks and Random forests\nFollow up from #3715: Neural networks and Random forests should have a checkbox `Replicable training` or something like this, which would decide whether random seed is fixed (to 0) or \"random\".\r\n\r\nIn Neural networks: add the check box.\r\n\r\nIn Random forest: remove the spin box.\n", "before_files": [{"content": "from AnyQt.QtCore import Qt\n\nfrom Orange.data import Table\nfrom Orange.modelling import RandomForestLearner\nfrom Orange.widgets import settings, gui\nfrom Orange.widgets.utils.owlearnerwidget import OWBaseLearner\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Msg\n\n\nclass OWRandomForest(OWBaseLearner):\n    name = \"Random Forest\"\n    description = \"Predict using an ensemble of decision trees.\"\n    icon = \"icons/RandomForest.svg\"\n    replaces = [\n        \"Orange.widgets.classify.owrandomforest.OWRandomForest\",\n        \"Orange.widgets.regression.owrandomforestregression.OWRandomForestRegression\",\n    ]\n    priority = 40\n    keywords = []\n\n    LEARNER = RandomForestLearner\n\n    n_estimators = settings.Setting(10)\n    max_features = settings.Setting(5)\n    use_max_features = settings.Setting(False)\n    random_state = settings.Setting(0)\n    use_random_state = settings.Setting(False)\n    max_depth = settings.Setting(3)\n    use_max_depth = settings.Setting(False)\n    min_samples_split = settings.Setting(5)\n    use_min_samples_split = settings.Setting(True)\n    index_output = settings.Setting(0)\n\n    class Error(OWBaseLearner.Error):\n        not_enough_features = Msg(\"Insufficient number of attributes ({})\")\n\n    def add_main_layout(self):\n        box = gui.vBox(self.controlArea, 'Basic Properties')\n        self.n_estimators_spin = gui.spin(\n            box, self, \"n_estimators\", minv=1, maxv=10000, controlWidth=80,\n            alignment=Qt.AlignRight, label=\"Number of trees: \",\n            callback=self.settings_changed)\n        self.max_features_spin = gui.spin(\n            box, self, \"max_features\", 2, 50, controlWidth=80,\n            label=\"Number of attributes considered at each split: \",\n            callback=self.settings_changed, checked=\"use_max_features\",\n            checkCallback=self.settings_changed, alignment=Qt.AlignRight,)\n        self.random_state_spin = gui.spin(\n            box, self, \"random_state\", 0, 2 ** 31 - 1, controlWidth=80,\n            label=\"Fixed seed for random generator: \", alignment=Qt.AlignRight,\n            callback=self.settings_changed, checked=\"use_random_state\",\n            checkCallback=self.settings_changed)\n\n        box = gui.vBox(self.controlArea, \"Growth Control\")\n        self.max_depth_spin = gui.spin(\n            box, self, \"max_depth\", 1, 50, controlWidth=80,\n            label=\"Limit depth of individual trees: \", alignment=Qt.AlignRight,\n            callback=self.settings_changed, checked=\"use_max_depth\",\n            checkCallback=self.settings_changed)\n        self.min_samples_split_spin = gui.spin(\n            box, self, \"min_samples_split\", 2, 1000, controlWidth=80,\n            label=\"Do not split subsets smaller than: \",\n            callback=self.settings_changed, checked=\"use_min_samples_split\",\n            checkCallback=self.settings_changed, alignment=Qt.AlignRight)\n\n    def create_learner(self):\n        common_args = {\"n_estimators\": self.n_estimators}\n        if self.use_max_features:\n            common_args[\"max_features\"] = self.max_features\n        if self.use_random_state:\n            common_args[\"random_state\"] = self.random_state\n        if self.use_max_depth:\n            common_args[\"max_depth\"] = self.max_depth\n        if self.use_min_samples_split:\n            common_args[\"min_samples_split\"] = self.min_samples_split\n\n        return self.LEARNER(preprocessors=self.preprocessors, **common_args)\n\n    def check_data(self):\n        self.Error.not_enough_features.clear()\n        if super().check_data():\n            n_features = len(self.data.domain.attributes)\n            if self.use_max_features and self.max_features > n_features:\n                self.Error.not_enough_features(n_features)\n                self.valid_data = False\n        return self.valid_data\n\n    def get_learner_parameters(self):\n        \"\"\"Called by send report to list the parameters of the learner.\"\"\"\n        return (\n            (\"Number of trees\", self.n_estimators),\n            (\"Maximal number of considered features\",\n             self.max_features if self.use_max_features else \"unlimited\"),\n            (\"Fixed random seed\", self.use_random_state and self.random_state),\n            (\"Maximal tree depth\",\n             self.max_depth if self.use_max_depth else \"unlimited\"),\n            (\"Stop splitting nodes with maximum instances\",\n             self.min_samples_split if self.use_min_samples_split else \"unlimited\")\n        )\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWRandomForest).run(Table(\"iris\"))\n", "path": "Orange/widgets/model/owrandomforest.py"}], "after_files": [{"content": "from AnyQt.QtCore import Qt\n\nfrom Orange.data import Table\nfrom Orange.modelling import RandomForestLearner\nfrom Orange.widgets import settings, gui\nfrom Orange.widgets.utils.owlearnerwidget import OWBaseLearner\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Msg\n\n\nclass OWRandomForest(OWBaseLearner):\n    name = \"Random Forest\"\n    description = \"Predict using an ensemble of decision trees.\"\n    icon = \"icons/RandomForest.svg\"\n    replaces = [\n        \"Orange.widgets.classify.owrandomforest.OWRandomForest\",\n        \"Orange.widgets.regression.owrandomforestregression.OWRandomForestRegression\",\n    ]\n    priority = 40\n    keywords = []\n\n    LEARNER = RandomForestLearner\n\n    n_estimators = settings.Setting(10)\n    max_features = settings.Setting(5)\n    use_max_features = settings.Setting(False)\n    use_random_state = settings.Setting(False)\n    max_depth = settings.Setting(3)\n    use_max_depth = settings.Setting(False)\n    min_samples_split = settings.Setting(5)\n    use_min_samples_split = settings.Setting(True)\n    index_output = settings.Setting(0)\n\n    class Error(OWBaseLearner.Error):\n        not_enough_features = Msg(\"Insufficient number of attributes ({})\")\n\n    def add_main_layout(self):\n        box = gui.vBox(self.controlArea, 'Basic Properties')\n        self.n_estimators_spin = gui.spin(\n            box, self, \"n_estimators\", minv=1, maxv=10000, controlWidth=80,\n            alignment=Qt.AlignRight, label=\"Number of trees: \",\n            callback=self.settings_changed)\n        self.max_features_spin = gui.spin(\n            box, self, \"max_features\", 2, 50, controlWidth=80,\n            label=\"Number of attributes considered at each split: \",\n            callback=self.settings_changed, checked=\"use_max_features\",\n            checkCallback=self.settings_changed, alignment=Qt.AlignRight,)\n        self.random_state = gui.checkBox(\n            box, self, \"use_random_state\", label=\"Replicable training\",\n            callback=self.settings_changed)\n\n        box = gui.vBox(self.controlArea, \"Growth Control\")\n        self.max_depth_spin = gui.spin(\n            box, self, \"max_depth\", 1, 50, controlWidth=80,\n            label=\"Limit depth of individual trees: \", alignment=Qt.AlignRight,\n            callback=self.settings_changed, checked=\"use_max_depth\",\n            checkCallback=self.settings_changed)\n        self.min_samples_split_spin = gui.spin(\n            box, self, \"min_samples_split\", 2, 1000, controlWidth=80,\n            label=\"Do not split subsets smaller than: \",\n            callback=self.settings_changed, checked=\"use_min_samples_split\",\n            checkCallback=self.settings_changed, alignment=Qt.AlignRight)\n\n    def create_learner(self):\n        common_args = {\"n_estimators\": self.n_estimators}\n        if self.use_max_features:\n            common_args[\"max_features\"] = self.max_features\n        if self.use_random_state:\n            common_args[\"random_state\"] = 0\n        if self.use_max_depth:\n            common_args[\"max_depth\"] = self.max_depth\n        if self.use_min_samples_split:\n            common_args[\"min_samples_split\"] = self.min_samples_split\n\n        return self.LEARNER(preprocessors=self.preprocessors, **common_args)\n\n    def check_data(self):\n        self.Error.not_enough_features.clear()\n        if super().check_data():\n            n_features = len(self.data.domain.attributes)\n            if self.use_max_features and self.max_features > n_features:\n                self.Error.not_enough_features(n_features)\n                self.valid_data = False\n        return self.valid_data\n\n    def get_learner_parameters(self):\n        \"\"\"Called by send report to list the parameters of the learner.\"\"\"\n        return (\n            (\"Number of trees\", self.n_estimators),\n            (\"Maximal number of considered features\",\n             self.max_features if self.use_max_features else \"unlimited\"),\n            (\"Replicable training\", [\"No\", \"Yes\"][self.use_random_state]),\n            (\"Maximal tree depth\",\n             self.max_depth if self.use_max_depth else \"unlimited\"),\n            (\"Stop splitting nodes with maximum instances\",\n             self.min_samples_split if self.use_min_samples_split else \"unlimited\")\n        )\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWRandomForest).run(Table(\"iris\"))\n", "path": "Orange/widgets/model/owrandomforest.py"}]}