We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
`None` categorical not visible on Slice plot
### Expected behavior

I have a categorical like this: `trial.suggest_categorical("class_weight", ["balanced", None])`

The slice plot shows the "balanced" value but not the `None` value.
I could write a workaround by using `"None"` as a string and then convert it to `None`
but I thing it could be nice if the real `None` is ploted.

See sceenshot:

<img width="234" alt="image" src="https://user-images.githubusercontent.com/229382/199188383-981f256d-0b66-4a1c-be40-68ecd6ae4528.png">


### Environment

- Optuna version:3.0.3
- Python version:3.9.13
- OS:Linux-5.10.0-17-amd64-x86_64-with-glibc2.31

### Error messages, stack traces, or logs

```shell
see screenshot
```


### Steps to reproduce

see description above

### Additional context (optional)

_No response_

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `optuna/visualization/_slice.py`
Content:
```
1 from typing import Any
2 from typing import Callable
3 from typing import cast
4 from typing import List
5 from typing import NamedTuple
6 from typing import Optional
7 
8 from optuna.logging import get_logger
9 from optuna.study import Study
10 from optuna.trial import FrozenTrial
11 from optuna.trial import TrialState
12 from optuna.visualization._plotly_imports import _imports
13 from optuna.visualization._utils import _check_plot_args
14 from optuna.visualization._utils import _filter_nonfinite
15 from optuna.visualization._utils import _is_log_scale
16 from optuna.visualization._utils import _is_numerical
17 
18 
19 if _imports.is_successful():
20     from optuna.visualization._plotly_imports import go
21     from optuna.visualization._plotly_imports import make_subplots
22     from optuna.visualization._plotly_imports import Scatter
23     from optuna.visualization._utils import COLOR_SCALE
24 
25 _logger = get_logger(__name__)
26 
27 
28 class _SliceSubplotInfo(NamedTuple):
29     param_name: str
30     x: List[Any]
31     y: List[float]
32     trial_numbers: List[int]
33     is_log: bool
34     is_numerical: bool
35 
36 
37 class _SlicePlotInfo(NamedTuple):
38     target_name: str
39     subplots: List[_SliceSubplotInfo]
40 
41 
42 def _get_slice_subplot_info(
43     trials: List[FrozenTrial],
44     param: str,
45     target: Optional[Callable[[FrozenTrial], float]],
46     log_scale: bool,
47     numerical: bool,
48 ) -> _SliceSubplotInfo:
49 
50     if target is None:
51 
52         def _target(t: FrozenTrial) -> float:
53             return cast(float, t.value)
54 
55         target = _target
56 
57     return _SliceSubplotInfo(
58         param_name=param,
59         x=[t.params[param] for t in trials if param in t.params],
60         y=[target(t) for t in trials if param in t.params],
61         trial_numbers=[t.number for t in trials if param in t.params],
62         is_log=log_scale,
63         is_numerical=numerical,
64     )
65 
66 
67 def _get_slice_plot_info(
68     study: Study,
69     params: Optional[List[str]],
70     target: Optional[Callable[[FrozenTrial], float]],
71     target_name: str,
72 ) -> _SlicePlotInfo:
73 
74     _check_plot_args(study, target, target_name)
75 
76     trials = _filter_nonfinite(
77         study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target
78     )
79 
80     if len(trials) == 0:
81         _logger.warning("Your study does not have any completed trials.")
82         return _SlicePlotInfo(target_name, [])
83 
84     all_params = {p_name for t in trials for p_name in t.params.keys()}
85     if params is None:
86         sorted_params = sorted(all_params)
87     else:
88         for input_p_name in params:
89             if input_p_name not in all_params:
90                 raise ValueError(f"Parameter {input_p_name} does not exist in your study.")
91         sorted_params = sorted(set(params))
92 
93     return _SlicePlotInfo(
94         target_name=target_name,
95         subplots=[
96             _get_slice_subplot_info(
97                 trials=trials,
98                 param=param,
99                 target=target,
100                 log_scale=_is_log_scale(trials, param),
101                 numerical=_is_numerical(trials, param),
102             )
103             for param in sorted_params
104         ],
105     )
106 
107 
108 def plot_slice(
109     study: Study,
110     params: Optional[List[str]] = None,
111     *,
112     target: Optional[Callable[[FrozenTrial], float]] = None,
113     target_name: str = "Objective Value",
114 ) -> "go.Figure":
115     """Plot the parameter relationship as slice plot in a study.
116 
117     Note that, if a parameter contains missing values, a trial with missing values is not plotted.
118 
119     Example:
120 
121         The following code snippet shows how to plot the parameter relationship as slice plot.
122 
123         .. plotly::
124 
125             import optuna
126 
127 
128             def objective(trial):
129                 x = trial.suggest_float("x", -100, 100)
130                 y = trial.suggest_categorical("y", [-1, 0, 1])
131                 return x ** 2 + y
132 
133 
134             sampler = optuna.samplers.TPESampler(seed=10)
135             study = optuna.create_study(sampler=sampler)
136             study.optimize(objective, n_trials=10)
137 
138             fig = optuna.visualization.plot_slice(study, params=["x", "y"])
139             fig.show()
140 
141     Args:
142         study:
143             A :class:`~optuna.study.Study` object whose trials are plotted for their target values.
144         params:
145             Parameter list to visualize. The default is all parameters.
146         target:
147             A function to specify the value to display. If it is :obj:`None` and ``study`` is being
148             used for single-objective optimization, the objective values are plotted.
149 
150             .. note::
151                 Specify this argument if ``study`` is being used for multi-objective optimization.
152         target_name:
153             Target's name to display on the axis label.
154 
155     Returns:
156         A :class:`plotly.graph_objs.Figure` object.
157     """
158 
159     _imports.check()
160     return _get_slice_plot(_get_slice_plot_info(study, params, target, target_name))
161 
162 
163 def _get_slice_plot(info: _SlicePlotInfo) -> "go.Figure":
164 
165     layout = go.Layout(title="Slice Plot")
166 
167     if len(info.subplots) == 0:
168         return go.Figure(data=[], layout=layout)
169     elif len(info.subplots) == 1:
170         figure = go.Figure(data=[_generate_slice_subplot(info.subplots[0])], layout=layout)
171         figure.update_xaxes(title_text=info.subplots[0].param_name)
172         figure.update_yaxes(title_text=info.target_name)
173         if info.subplots[0].is_log:
174             figure.update_xaxes(type="log")
175     else:
176         figure = make_subplots(rows=1, cols=len(info.subplots), shared_yaxes=True)
177         figure.update_layout(layout)
178         showscale = True  # showscale option only needs to be specified once.
179         for column_index, subplot_info in enumerate(info.subplots, start=1):
180             trace = _generate_slice_subplot(subplot_info)
181             trace.update(marker={"showscale": showscale})  # showscale's default is True.
182             if showscale:
183                 showscale = False
184             figure.add_trace(trace, row=1, col=column_index)
185             figure.update_xaxes(title_text=subplot_info.param_name, row=1, col=column_index)
186             if column_index == 1:
187                 figure.update_yaxes(title_text=info.target_name, row=1, col=column_index)
188             if subplot_info.is_log:
189                 figure.update_xaxes(type="log", row=1, col=column_index)
190         if len(info.subplots) > 3:
191             # Ensure that each subplot has a minimum width without relying on autusizing.
192             figure.update_layout(width=300 * len(info.subplots))
193 
194     return figure
195 
196 
197 def _generate_slice_subplot(subplot_info: _SliceSubplotInfo) -> "Scatter":
198     return go.Scatter(
199         x=subplot_info.x,
200         y=subplot_info.y,
201         mode="markers",
202         marker={
203             "line": {"width": 0.5, "color": "Grey"},
204             "color": subplot_info.trial_numbers,
205             "colorscale": COLOR_SCALE,
206             "colorbar": {
207                 "title": "Trial",
208                 "x": 1.0,  # Offset the colorbar position with a fixed width `xpad`.
209                 "xpad": 40,
210             },
211         },
212         showlegend=False,
213     )
214
```
--- 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/optuna/visualization/_slice.py b/optuna/visualization/_slice.py
--- a/optuna/visualization/_slice.py
+++ b/optuna/visualization/_slice.py
@@ -195,9 +195,12 @@
 
 
 def _generate_slice_subplot(subplot_info: _SliceSubplotInfo) -> "Scatter":
+    x = [x if x is not None else "None" for x in subplot_info.x]
+    y = [y if y is not None else "None" for y in subplot_info.y]
+
     return go.Scatter(
-        x=subplot_info.x,
-        y=subplot_info.y,
+        x=x,
+        y=y,
         mode="markers",
         marker={
             "line": {"width": 0.5, "color": "Grey"},

{"golden_diff": "diff --git a/optuna/visualization/_slice.py b/optuna/visualization/_slice.py\n--- a/optuna/visualization/_slice.py\n+++ b/optuna/visualization/_slice.py\n@@ -195,9 +195,12 @@\n \n \n def _generate_slice_subplot(subplot_info: _SliceSubplotInfo) -> \"Scatter\":\n+    x = [x if x is not None else \"None\" for x in subplot_info.x]\n+    y = [y if y is not None else \"None\" for y in subplot_info.y]\n+\n     return go.Scatter(\n-        x=subplot_info.x,\n-        y=subplot_info.y,\n+        x=x,\n+        y=y,\n         mode=\"markers\",\n         marker={\n             \"line\": {\"width\": 0.5, \"color\": \"Grey\"},\n", "issue": "`None` categorical not visible on Slice plot\n### Expected behavior\r\n\r\nI have a categorical like this: `trial.suggest_categorical(\"class_weight\", [\"balanced\", None])`\r\n\r\nThe slice plot shows the \"balanced\" value but not the `None` value.\r\nI could write a workaround by using `\"None\"` as a string and then convert it to `None`\r\nbut I thing it could be nice if the real `None` is ploted.\r\n\r\nSee sceenshot:\r\n\r\n<img width=\"234\" alt=\"image\" src=\"https://user-images.githubusercontent.com/229382/199188383-981f256d-0b66-4a1c-be40-68ecd6ae4528.png\">\r\n\r\n\r\n### Environment\r\n\r\n- Optuna version:3.0.3\r\n- Python version:3.9.13\r\n- OS:Linux-5.10.0-17-amd64-x86_64-with-glibc2.31\r\n\r\n### Error messages, stack traces, or logs\r\n\r\n```shell\r\nsee screenshot\r\n```\r\n\r\n\r\n### Steps to reproduce\r\n\r\nsee description above\r\n\r\n### Additional context (optional)\r\n\r\n_No response_\n", "before_files": [{"content": "from typing import Any\nfrom typing import Callable\nfrom typing import cast\nfrom typing import List\nfrom typing import NamedTuple\nfrom typing import Optional\n\nfrom optuna.logging import get_logger\nfrom optuna.study import Study\nfrom optuna.trial import FrozenTrial\nfrom optuna.trial import TrialState\nfrom optuna.visualization._plotly_imports import _imports\nfrom optuna.visualization._utils import _check_plot_args\nfrom optuna.visualization._utils import _filter_nonfinite\nfrom optuna.visualization._utils import _is_log_scale\nfrom optuna.visualization._utils import _is_numerical\n\n\nif _imports.is_successful():\n    from optuna.visualization._plotly_imports import go\n    from optuna.visualization._plotly_imports import make_subplots\n    from optuna.visualization._plotly_imports import Scatter\n    from optuna.visualization._utils import COLOR_SCALE\n\n_logger = get_logger(__name__)\n\n\nclass _SliceSubplotInfo(NamedTuple):\n    param_name: str\n    x: List[Any]\n    y: List[float]\n    trial_numbers: List[int]\n    is_log: bool\n    is_numerical: bool\n\n\nclass _SlicePlotInfo(NamedTuple):\n    target_name: str\n    subplots: List[_SliceSubplotInfo]\n\n\ndef _get_slice_subplot_info(\n    trials: List[FrozenTrial],\n    param: str,\n    target: Optional[Callable[[FrozenTrial], float]],\n    log_scale: bool,\n    numerical: bool,\n) -> _SliceSubplotInfo:\n\n    if target is None:\n\n        def _target(t: FrozenTrial) -> float:\n            return cast(float, t.value)\n\n        target = _target\n\n    return _SliceSubplotInfo(\n        param_name=param,\n        x=[t.params[param] for t in trials if param in t.params],\n        y=[target(t) for t in trials if param in t.params],\n        trial_numbers=[t.number for t in trials if param in t.params],\n        is_log=log_scale,\n        is_numerical=numerical,\n    )\n\n\ndef _get_slice_plot_info(\n    study: Study,\n    params: Optional[List[str]],\n    target: Optional[Callable[[FrozenTrial], float]],\n    target_name: str,\n) -> _SlicePlotInfo:\n\n    _check_plot_args(study, target, target_name)\n\n    trials = _filter_nonfinite(\n        study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target\n    )\n\n    if len(trials) == 0:\n        _logger.warning(\"Your study does not have any completed trials.\")\n        return _SlicePlotInfo(target_name, [])\n\n    all_params = {p_name for t in trials for p_name in t.params.keys()}\n    if params is None:\n        sorted_params = sorted(all_params)\n    else:\n        for input_p_name in params:\n            if input_p_name not in all_params:\n                raise ValueError(f\"Parameter {input_p_name} does not exist in your study.\")\n        sorted_params = sorted(set(params))\n\n    return _SlicePlotInfo(\n        target_name=target_name,\n        subplots=[\n            _get_slice_subplot_info(\n                trials=trials,\n                param=param,\n                target=target,\n                log_scale=_is_log_scale(trials, param),\n                numerical=_is_numerical(trials, param),\n            )\n            for param in sorted_params\n        ],\n    )\n\n\ndef plot_slice(\n    study: Study,\n    params: Optional[List[str]] = None,\n    *,\n    target: Optional[Callable[[FrozenTrial], float]] = None,\n    target_name: str = \"Objective Value\",\n) -> \"go.Figure\":\n    \"\"\"Plot the parameter relationship as slice plot in a study.\n\n    Note that, if a parameter contains missing values, a trial with missing values is not plotted.\n\n    Example:\n\n        The following code snippet shows how to plot the parameter relationship as slice plot.\n\n        .. plotly::\n\n            import optuna\n\n\n            def objective(trial):\n                x = trial.suggest_float(\"x\", -100, 100)\n                y = trial.suggest_categorical(\"y\", [-1, 0, 1])\n                return x ** 2 + y\n\n\n            sampler = optuna.samplers.TPESampler(seed=10)\n            study = optuna.create_study(sampler=sampler)\n            study.optimize(objective, n_trials=10)\n\n            fig = optuna.visualization.plot_slice(study, params=[\"x\", \"y\"])\n            fig.show()\n\n    Args:\n        study:\n            A :class:`~optuna.study.Study` object whose trials are plotted for their target values.\n        params:\n            Parameter list to visualize. The default is all parameters.\n        target:\n            A function to specify the value to display. If it is :obj:`None` and ``study`` is being\n            used for single-objective optimization, the objective values are plotted.\n\n            .. note::\n                Specify this argument if ``study`` is being used for multi-objective optimization.\n        target_name:\n            Target's name to display on the axis label.\n\n    Returns:\n        A :class:`plotly.graph_objs.Figure` object.\n    \"\"\"\n\n    _imports.check()\n    return _get_slice_plot(_get_slice_plot_info(study, params, target, target_name))\n\n\ndef _get_slice_plot(info: _SlicePlotInfo) -> \"go.Figure\":\n\n    layout = go.Layout(title=\"Slice Plot\")\n\n    if len(info.subplots) == 0:\n        return go.Figure(data=[], layout=layout)\n    elif len(info.subplots) == 1:\n        figure = go.Figure(data=[_generate_slice_subplot(info.subplots[0])], layout=layout)\n        figure.update_xaxes(title_text=info.subplots[0].param_name)\n        figure.update_yaxes(title_text=info.target_name)\n        if info.subplots[0].is_log:\n            figure.update_xaxes(type=\"log\")\n    else:\n        figure = make_subplots(rows=1, cols=len(info.subplots), shared_yaxes=True)\n        figure.update_layout(layout)\n        showscale = True  # showscale option only needs to be specified once.\n        for column_index, subplot_info in enumerate(info.subplots, start=1):\n            trace = _generate_slice_subplot(subplot_info)\n            trace.update(marker={\"showscale\": showscale})  # showscale's default is True.\n            if showscale:\n                showscale = False\n            figure.add_trace(trace, row=1, col=column_index)\n            figure.update_xaxes(title_text=subplot_info.param_name, row=1, col=column_index)\n            if column_index == 1:\n                figure.update_yaxes(title_text=info.target_name, row=1, col=column_index)\n            if subplot_info.is_log:\n                figure.update_xaxes(type=\"log\", row=1, col=column_index)\n        if len(info.subplots) > 3:\n            # Ensure that each subplot has a minimum width without relying on autusizing.\n            figure.update_layout(width=300 * len(info.subplots))\n\n    return figure\n\n\ndef _generate_slice_subplot(subplot_info: _SliceSubplotInfo) -> \"Scatter\":\n    return go.Scatter(\n        x=subplot_info.x,\n        y=subplot_info.y,\n        mode=\"markers\",\n        marker={\n            \"line\": {\"width\": 0.5, \"color\": \"Grey\"},\n            \"color\": subplot_info.trial_numbers,\n            \"colorscale\": COLOR_SCALE,\n            \"colorbar\": {\n                \"title\": \"Trial\",\n                \"x\": 1.0,  # Offset the colorbar position with a fixed width `xpad`.\n                \"xpad\": 40,\n            },\n        },\n        showlegend=False,\n    )\n", "path": "optuna/visualization/_slice.py"}], "after_files": [{"content": "from typing import Any\nfrom typing import Callable\nfrom typing import cast\nfrom typing import List\nfrom typing import NamedTuple\nfrom typing import Optional\n\nfrom optuna.logging import get_logger\nfrom optuna.study import Study\nfrom optuna.trial import FrozenTrial\nfrom optuna.trial import TrialState\nfrom optuna.visualization._plotly_imports import _imports\nfrom optuna.visualization._utils import _check_plot_args\nfrom optuna.visualization._utils import _filter_nonfinite\nfrom optuna.visualization._utils import _is_log_scale\nfrom optuna.visualization._utils import _is_numerical\n\n\nif _imports.is_successful():\n    from optuna.visualization._plotly_imports import go\n    from optuna.visualization._plotly_imports import make_subplots\n    from optuna.visualization._plotly_imports import Scatter\n    from optuna.visualization._utils import COLOR_SCALE\n\n_logger = get_logger(__name__)\n\n\nclass _SliceSubplotInfo(NamedTuple):\n    param_name: str\n    x: List[Any]\n    y: List[float]\n    trial_numbers: List[int]\n    is_log: bool\n    is_numerical: bool\n\n\nclass _SlicePlotInfo(NamedTuple):\n    target_name: str\n    subplots: List[_SliceSubplotInfo]\n\n\ndef _get_slice_subplot_info(\n    trials: List[FrozenTrial],\n    param: str,\n    target: Optional[Callable[[FrozenTrial], float]],\n    log_scale: bool,\n    numerical: bool,\n) -> _SliceSubplotInfo:\n\n    if target is None:\n\n        def _target(t: FrozenTrial) -> float:\n            return cast(float, t.value)\n\n        target = _target\n\n    return _SliceSubplotInfo(\n        param_name=param,\n        x=[t.params[param] for t in trials if param in t.params],\n        y=[target(t) for t in trials if param in t.params],\n        trial_numbers=[t.number for t in trials if param in t.params],\n        is_log=log_scale,\n        is_numerical=numerical,\n    )\n\n\ndef _get_slice_plot_info(\n    study: Study,\n    params: Optional[List[str]],\n    target: Optional[Callable[[FrozenTrial], float]],\n    target_name: str,\n) -> _SlicePlotInfo:\n\n    _check_plot_args(study, target, target_name)\n\n    trials = _filter_nonfinite(\n        study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target\n    )\n\n    if len(trials) == 0:\n        _logger.warning(\"Your study does not have any completed trials.\")\n        return _SlicePlotInfo(target_name, [])\n\n    all_params = {p_name for t in trials for p_name in t.params.keys()}\n    if params is None:\n        sorted_params = sorted(all_params)\n    else:\n        for input_p_name in params:\n            if input_p_name not in all_params:\n                raise ValueError(f\"Parameter {input_p_name} does not exist in your study.\")\n        sorted_params = sorted(set(params))\n\n    return _SlicePlotInfo(\n        target_name=target_name,\n        subplots=[\n            _get_slice_subplot_info(\n                trials=trials,\n                param=param,\n                target=target,\n                log_scale=_is_log_scale(trials, param),\n                numerical=_is_numerical(trials, param),\n            )\n            for param in sorted_params\n        ],\n    )\n\n\ndef plot_slice(\n    study: Study,\n    params: Optional[List[str]] = None,\n    *,\n    target: Optional[Callable[[FrozenTrial], float]] = None,\n    target_name: str = \"Objective Value\",\n) -> \"go.Figure\":\n    \"\"\"Plot the parameter relationship as slice plot in a study.\n\n    Note that, if a parameter contains missing values, a trial with missing values is not plotted.\n\n    Example:\n\n        The following code snippet shows how to plot the parameter relationship as slice plot.\n\n        .. plotly::\n\n            import optuna\n\n\n            def objective(trial):\n                x = trial.suggest_float(\"x\", -100, 100)\n                y = trial.suggest_categorical(\"y\", [-1, 0, 1])\n                return x ** 2 + y\n\n\n            sampler = optuna.samplers.TPESampler(seed=10)\n            study = optuna.create_study(sampler=sampler)\n            study.optimize(objective, n_trials=10)\n\n            fig = optuna.visualization.plot_slice(study, params=[\"x\", \"y\"])\n            fig.show()\n\n    Args:\n        study:\n            A :class:`~optuna.study.Study` object whose trials are plotted for their target values.\n        params:\n            Parameter list to visualize. The default is all parameters.\n        target:\n            A function to specify the value to display. If it is :obj:`None` and ``study`` is being\n            used for single-objective optimization, the objective values are plotted.\n\n            .. note::\n                Specify this argument if ``study`` is being used for multi-objective optimization.\n        target_name:\n            Target's name to display on the axis label.\n\n    Returns:\n        A :class:`plotly.graph_objs.Figure` object.\n    \"\"\"\n\n    _imports.check()\n    return _get_slice_plot(_get_slice_plot_info(study, params, target, target_name))\n\n\ndef _get_slice_plot(info: _SlicePlotInfo) -> \"go.Figure\":\n\n    layout = go.Layout(title=\"Slice Plot\")\n\n    if len(info.subplots) == 0:\n        return go.Figure(data=[], layout=layout)\n    elif len(info.subplots) == 1:\n        figure = go.Figure(data=[_generate_slice_subplot(info.subplots[0])], layout=layout)\n        figure.update_xaxes(title_text=info.subplots[0].param_name)\n        figure.update_yaxes(title_text=info.target_name)\n        if info.subplots[0].is_log:\n            figure.update_xaxes(type=\"log\")\n    else:\n        figure = make_subplots(rows=1, cols=len(info.subplots), shared_yaxes=True)\n        figure.update_layout(layout)\n        showscale = True  # showscale option only needs to be specified once.\n        for column_index, subplot_info in enumerate(info.subplots, start=1):\n            trace = _generate_slice_subplot(subplot_info)\n            trace.update(marker={\"showscale\": showscale})  # showscale's default is True.\n            if showscale:\n                showscale = False\n            figure.add_trace(trace, row=1, col=column_index)\n            figure.update_xaxes(title_text=subplot_info.param_name, row=1, col=column_index)\n            if column_index == 1:\n                figure.update_yaxes(title_text=info.target_name, row=1, col=column_index)\n            if subplot_info.is_log:\n                figure.update_xaxes(type=\"log\", row=1, col=column_index)\n        if len(info.subplots) > 3:\n            # Ensure that each subplot has a minimum width without relying on autusizing.\n            figure.update_layout(width=300 * len(info.subplots))\n\n    return figure\n\n\ndef _generate_slice_subplot(subplot_info: _SliceSubplotInfo) -> \"Scatter\":\n    x = [x if x is not None else \"None\" for x in subplot_info.x]\n    y = [y if y is not None else \"None\" for y in subplot_info.y]\n\n    return go.Scatter(\n        x=x,\n        y=y,\n        mode=\"markers\",\n        marker={\n            \"line\": {\"width\": 0.5, \"color\": \"Grey\"},\n            \"color\": subplot_info.trial_numbers,\n            \"colorscale\": COLOR_SCALE,\n            \"colorbar\": {\n                \"title\": \"Trial\",\n                \"x\": 1.0,  # Offset the colorbar position with a fixed width `xpad`.\n                \"xpad\": 40,\n            },\n        },\n        showlegend=False,\n    )\n", "path": "optuna/visualization/_slice.py"}]}