We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[BUG] Add copy button to code snippets
We used to have this:
![image](https://user-images.githubusercontent.com/33841818/168883725-41ce6768-925f-4d62-8de7-b1e77502d685.png)

For all code snippets (currently both in sphinx-gallery files. Doesn't appear anymore.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 """
3 Data Integrity Suite on Avocado Sales Data - Quickstart
4 *******************************************************
5 
6 The deepchecks integrity suite is relevant any time you have data that you wish to validate:
7 whether it's on a fresh batch of data, or right before splitting it or using it for training. 
8 Here we'll use the avocado prices dataset, to demonstrate how you can run
9 the suite with only a few simple lines of code, and see which kind of insights it can find.
10 
11 .. code-block:: bash
12 
13     # Before we start, if you don't have deepchecks installed yet,
14     # make sure to run:
15     pip install deepchecks -U --quiet #--user
16 """
17 
18 #%%
19 # Load and Prepare Data
20 # ====================================================
21 
22 from deepchecks.tabular import datasets
23 
24 # load data
25 data = datasets.regression.avocado.load_data(data_format='DataFrame', as_train_test=False)
26 #%%
27 
28 # drop unused columns (remove after fix...)
29 data = data.drop(columns=['Unnamed: 0'])
30 
31 #%%
32 # Insert a few typcial problems to dataset for demonstration.
33 
34 import pandas as pd
35 
36 def add_dirty_data(df):
37     # change strings
38     df.loc[df[df['type'] == 'organic'].sample(frac=0.18).index,'type'] = 'Organic'
39     df.loc[df[df['type'] == 'organic'].sample(frac=0.01).index,'type'] = 'ORGANIC'
40     # add duplicates
41     df = pd.concat([df, df.sample(frac=0.156)], axis=0, ignore_index=True)
42     # add column with single value
43     df['Is Ripe'] = True
44     return df
45 
46 
47 dirty_df = add_dirty_data(data)
48 
49 #%%
50 # Run Deepchecks for Data Integrity
51 # ====================================
52 #
53 # Define a Dataset Object
54 # ------------------------
55 #
56 # Create a deepchecks Dataset, including the relevant metadata (label, date, index, etc.).
57 # Check out :class:`deepchecks.tabular.Dataset` to see all of the columns that can be declared.
58 
59 from deepchecks.tabular import Dataset
60 
61 # We explicitly state the categorical features,
62 # otherwise they will be automatically inferred, which may not work perfectly and is not recommended.
63 # The label can be passed as a column name or a separate pd.Series / pd.DataFrame
64 ds = Dataset(dirty_df, cat_features = ['type'], datetime_name='Date', label = 'AveragePrice')
65 
66 #%%
67 # Run the Deepchecks Suite
68 # --------------------------
69 #
70 # Validate your data with the :class:`deepchecks.tabular.suites.single_dataset_integrity` suite.
71 # It runs on a single dataset, so you can run it on any batch of data (e.g. train data, test data, a new batch of data
72 # that recently arrived)
73 #
74 # Check out the :doc:`when should you use </getting-started/when_should_you_use>`
75 # deepchecks guide for some more info about the existing suites and when to use them.
76 
77 from deepchecks.tabular.suites import data_integrity
78 
79 # Run Suite:
80 integ_suite = data_integrity()
81 integ_suite.run(ds)
82 
83 #%%
84 # We can inspect the suite outputs and see that there are a few problems we'd like to fix.
85 # We'll now fix them and check that they're resolved by re-running those specific checks.
86 
87 
88 #%%
89 # Run a Single Check
90 # -------------------
91 # We can run a single check on a dataset, and see the results.
92 
93 from deepchecks.tabular.checks import IsSingleValue, DataDuplicates
94 
95 # first let's see how the check runs:
96 IsSingleValue().run(ds)
97 
98 #%%
99 
100 # we can also add a condition:
101 single_value_with_condition = IsSingleValue().add_condition_not_single_value()
102 result = single_value_with_condition.run(ds)
103 result
104 
105 #%%
106 
107 # We can also inspect and use the result's value:
108 result.value
109 
110 #%%
111 # Now let's remove the single value column and rerun (notice that we're using directly 
112 # the ``data`` attribute that stores the dataframe inside the Dataset)
113 
114 ds.data.drop('Is Ripe', axis=1, inplace=True)
115 result = single_value_with_condition.run(ds)
116 result
117 
118 #%%
119 
120 # Alternatively we can fix the dataframe directly, and create a new dataset.
121 # Let's fix also the duplicate values:
122 dirty_df.drop_duplicates(inplace=True)
123 dirty_df.drop('Is Ripe', axis=1, inplace=True)
124 ds = Dataset(dirty_df, cat_features=['type'], datetime_name='Date', label='AveragePrice')
125 result = DataDuplicates().add_condition_ratio_not_greater_than(0).run(ds)
126 result
127 
128 #%%
129 # Rerun Suite on the Fixed Dataset
130 # ---------------------------------
131 # Finally, we'll choose to keep the "organic" multiple spellings as they represent different sources.
132 # So we'll customaize the suite by removing the condition from it (or delete check completely).
133 # Alternatively - we can customize it by creating a new Suite with the desired checks and conditions.
134 # See :doc:`/user-guide/general/customizations/examples/customizing-suites` for more info.
135 
136 # let's inspect the suite's structure
137 integ_suite
138 
139 #%%
140 
141 # and remove the condition:
142 integ_suite[3].clean_conditions()
143 
144 #%%
145 # Now we can re-run the suite using:
146 integ_suite.run(ds)
147 
148 #%%
149 # and all of the conditions will pass.
150 #
151 # *Note: the check we manipulated will still run as part of the Suite, however
152 # it won't appear in the Conditions Summary since it no longer has any
153 # conditions defined on it. You can still see its display results in the 
154 # Additional Outputs section*
155 #
156 # For more info about working with conditions, see the detailed
157 # :doc:`/user-guide/general/customizations/examples/plot_configure_checks_conditions' guide.
158
```
--- 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/docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py b/docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py
--- a/docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py
+++ b/docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py
@@ -143,7 +143,7 @@
 
 #%%
 # Now we can re-run the suite using:
-integ_suite.run(ds)
+res = integ_suite.run(ds)
 
 #%%
 # and all of the conditions will pass.

{"golden_diff": "diff --git a/docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py b/docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py\n--- a/docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py\n+++ b/docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py\n@@ -143,7 +143,7 @@\n \n #%%\n # Now we can re-run the suite using:\n-integ_suite.run(ds)\n+res = integ_suite.run(ds)\n \n #%%\n # and all of the conditions will pass.\n", "issue": "[BUG] Add copy button to code snippets\nWe used to have this:\r\n![image](https://user-images.githubusercontent.com/33841818/168883725-41ce6768-925f-4d62-8de7-b1e77502d685.png)\r\n\r\nFor all code snippets (currently both in sphinx-gallery files. Doesn't appear anymore.\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\n\"\"\"\nData Integrity Suite on Avocado Sales Data - Quickstart\n*******************************************************\n\nThe deepchecks integrity suite is relevant any time you have data that you wish to validate:\nwhether it's on a fresh batch of data, or right before splitting it or using it for training. \nHere we'll use the avocado prices dataset, to demonstrate how you can run\nthe suite with only a few simple lines of code, and see which kind of insights it can find.\n\n.. code-block:: bash\n\n    # Before we start, if you don't have deepchecks installed yet,\n    # make sure to run:\n    pip install deepchecks -U --quiet #--user\n\"\"\"\n\n#%%\n# Load and Prepare Data\n# ====================================================\n\nfrom deepchecks.tabular import datasets\n\n# load data\ndata = datasets.regression.avocado.load_data(data_format='DataFrame', as_train_test=False)\n#%%\n\n# drop unused columns (remove after fix...)\ndata = data.drop(columns=['Unnamed: 0'])\n\n#%%\n# Insert a few typcial problems to dataset for demonstration.\n\nimport pandas as pd\n\ndef add_dirty_data(df):\n    # change strings\n    df.loc[df[df['type'] == 'organic'].sample(frac=0.18).index,'type'] = 'Organic'\n    df.loc[df[df['type'] == 'organic'].sample(frac=0.01).index,'type'] = 'ORGANIC'\n    # add duplicates\n    df = pd.concat([df, df.sample(frac=0.156)], axis=0, ignore_index=True)\n    # add column with single value\n    df['Is Ripe'] = True\n    return df\n\n\ndirty_df = add_dirty_data(data)\n\n#%%\n# Run Deepchecks for Data Integrity\n# ====================================\n#\n# Define a Dataset Object\n# ------------------------\n#\n# Create a deepchecks Dataset, including the relevant metadata (label, date, index, etc.).\n# Check out :class:`deepchecks.tabular.Dataset` to see all of the columns that can be declared.\n\nfrom deepchecks.tabular import Dataset\n\n# We explicitly state the categorical features,\n# otherwise they will be automatically inferred, which may not work perfectly and is not recommended.\n# The label can be passed as a column name or a separate pd.Series / pd.DataFrame\nds = Dataset(dirty_df, cat_features = ['type'], datetime_name='Date', label = 'AveragePrice')\n\n#%%\n# Run the Deepchecks Suite\n# --------------------------\n#\n# Validate your data with the :class:`deepchecks.tabular.suites.single_dataset_integrity` suite.\n# It runs on a single dataset, so you can run it on any batch of data (e.g. train data, test data, a new batch of data\n# that recently arrived)\n#\n# Check out the :doc:`when should you use </getting-started/when_should_you_use>`\n# deepchecks guide for some more info about the existing suites and when to use them.\n\nfrom deepchecks.tabular.suites import data_integrity\n\n# Run Suite:\ninteg_suite = data_integrity()\ninteg_suite.run(ds)\n\n#%%\n# We can inspect the suite outputs and see that there are a few problems we'd like to fix.\n# We'll now fix them and check that they're resolved by re-running those specific checks.\n\n\n#%%\n# Run a Single Check\n# -------------------\n# We can run a single check on a dataset, and see the results.\n\nfrom deepchecks.tabular.checks import IsSingleValue, DataDuplicates\n\n# first let's see how the check runs:\nIsSingleValue().run(ds)\n\n#%%\n\n# we can also add a condition:\nsingle_value_with_condition = IsSingleValue().add_condition_not_single_value()\nresult = single_value_with_condition.run(ds)\nresult\n\n#%%\n\n# We can also inspect and use the result's value:\nresult.value\n\n#%%\n# Now let's remove the single value column and rerun (notice that we're using directly \n# the ``data`` attribute that stores the dataframe inside the Dataset)\n\nds.data.drop('Is Ripe', axis=1, inplace=True)\nresult = single_value_with_condition.run(ds)\nresult\n\n#%%\n\n# Alternatively we can fix the dataframe directly, and create a new dataset.\n# Let's fix also the duplicate values:\ndirty_df.drop_duplicates(inplace=True)\ndirty_df.drop('Is Ripe', axis=1, inplace=True)\nds = Dataset(dirty_df, cat_features=['type'], datetime_name='Date', label='AveragePrice')\nresult = DataDuplicates().add_condition_ratio_not_greater_than(0).run(ds)\nresult\n\n#%%\n# Rerun Suite on the Fixed Dataset\n# ---------------------------------\n# Finally, we'll choose to keep the \"organic\" multiple spellings as they represent different sources.\n# So we'll customaize the suite by removing the condition from it (or delete check completely).\n# Alternatively - we can customize it by creating a new Suite with the desired checks and conditions.\n# See :doc:`/user-guide/general/customizations/examples/customizing-suites` for more info.\n\n# let's inspect the suite's structure\ninteg_suite\n\n#%%\n\n# and remove the condition:\ninteg_suite[3].clean_conditions()\n\n#%%\n# Now we can re-run the suite using:\ninteg_suite.run(ds)\n\n#%%\n# and all of the conditions will pass.\n#\n# *Note: the check we manipulated will still run as part of the Suite, however\n# it won't appear in the Conditions Summary since it no longer has any\n# conditions defined on it. You can still see its display results in the \n# Additional Outputs section*\n#\n# For more info about working with conditions, see the detailed\n# :doc:`/user-guide/general/customizations/examples/plot_configure_checks_conditions' guide.\n", "path": "docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\n\"\"\"\nData Integrity Suite on Avocado Sales Data - Quickstart\n*******************************************************\n\nThe deepchecks integrity suite is relevant any time you have data that you wish to validate:\nwhether it's on a fresh batch of data, or right before splitting it or using it for training. \nHere we'll use the avocado prices dataset, to demonstrate how you can run\nthe suite with only a few simple lines of code, and see which kind of insights it can find.\n\n.. code-block:: bash\n\n    # Before we start, if you don't have deepchecks installed yet,\n    # make sure to run:\n    pip install deepchecks -U --quiet #--user\n\"\"\"\n\n#%%\n# Load and Prepare Data\n# ====================================================\n\nfrom deepchecks.tabular import datasets\n\n# load data\ndata = datasets.regression.avocado.load_data(data_format='DataFrame', as_train_test=False)\n#%%\n\n# drop unused columns (remove after fix...)\ndata = data.drop(columns=['Unnamed: 0'])\n\n#%%\n# Insert a few typcial problems to dataset for demonstration.\n\nimport pandas as pd\n\ndef add_dirty_data(df):\n    # change strings\n    df.loc[df[df['type'] == 'organic'].sample(frac=0.18).index,'type'] = 'Organic'\n    df.loc[df[df['type'] == 'organic'].sample(frac=0.01).index,'type'] = 'ORGANIC'\n    # add duplicates\n    df = pd.concat([df, df.sample(frac=0.156)], axis=0, ignore_index=True)\n    # add column with single value\n    df['Is Ripe'] = True\n    return df\n\n\ndirty_df = add_dirty_data(data)\n\n#%%\n# Run Deepchecks for Data Integrity\n# ====================================\n#\n# Define a Dataset Object\n# ------------------------\n#\n# Create a deepchecks Dataset, including the relevant metadata (label, date, index, etc.).\n# Check out :class:`deepchecks.tabular.Dataset` to see all of the columns that can be declared.\n\nfrom deepchecks.tabular import Dataset\n\n# We explicitly state the categorical features,\n# otherwise they will be automatically inferred, which may not work perfectly and is not recommended.\n# The label can be passed as a column name or a separate pd.Series / pd.DataFrame\nds = Dataset(dirty_df, cat_features = ['type'], datetime_name='Date', label = 'AveragePrice')\n\n#%%\n# Run the Deepchecks Suite\n# --------------------------\n#\n# Validate your data with the :class:`deepchecks.tabular.suites.single_dataset_integrity` suite.\n# It runs on a single dataset, so you can run it on any batch of data (e.g. train data, test data, a new batch of data\n# that recently arrived)\n#\n# Check out the :doc:`when should you use </getting-started/when_should_you_use>`\n# deepchecks guide for some more info about the existing suites and when to use them.\n\nfrom deepchecks.tabular.suites import data_integrity\n\n# Run Suite:\ninteg_suite = data_integrity()\ninteg_suite.run(ds)\n\n#%%\n# We can inspect the suite outputs and see that there are a few problems we'd like to fix.\n# We'll now fix them and check that they're resolved by re-running those specific checks.\n\n\n#%%\n# Run a Single Check\n# -------------------\n# We can run a single check on a dataset, and see the results.\n\nfrom deepchecks.tabular.checks import IsSingleValue, DataDuplicates\n\n# first let's see how the check runs:\nIsSingleValue().run(ds)\n\n#%%\n\n# we can also add a condition:\nsingle_value_with_condition = IsSingleValue().add_condition_not_single_value()\nresult = single_value_with_condition.run(ds)\nresult\n\n#%%\n\n# We can also inspect and use the result's value:\nresult.value\n\n#%%\n# Now let's remove the single value column and rerun (notice that we're using directly \n# the ``data`` attribute that stores the dataframe inside the Dataset)\n\nds.data.drop('Is Ripe', axis=1, inplace=True)\nresult = single_value_with_condition.run(ds)\nresult\n\n#%%\n\n# Alternatively we can fix the dataframe directly, and create a new dataset.\n# Let's fix also the duplicate values:\ndirty_df.drop_duplicates(inplace=True)\ndirty_df.drop('Is Ripe', axis=1, inplace=True)\nds = Dataset(dirty_df, cat_features=['type'], datetime_name='Date', label='AveragePrice')\nresult = DataDuplicates().add_condition_ratio_not_greater_than(0).run(ds)\nresult\n\n#%%\n# Rerun Suite on the Fixed Dataset\n# ---------------------------------\n# Finally, we'll choose to keep the \"organic\" multiple spellings as they represent different sources.\n# So we'll customaize the suite by removing the condition from it (or delete check completely).\n# Alternatively - we can customize it by creating a new Suite with the desired checks and conditions.\n# See :doc:`/user-guide/general/customizations/examples/customizing-suites` for more info.\n\n# let's inspect the suite's structure\ninteg_suite\n\n#%%\n\n# and remove the condition:\ninteg_suite[3].clean_conditions()\n\n#%%\n# Now we can re-run the suite using:\nres = integ_suite.run(ds)\n\n#%%\n# and all of the conditions will pass.\n#\n# *Note: the check we manipulated will still run as part of the Suite, however\n# it won't appear in the Conditions Summary since it no longer has any\n# conditions defined on it. You can still see its display results in the \n# Additional Outputs section*\n#\n# For more info about working with conditions, see the detailed\n# :doc:`/user-guide/general/customizations/examples/plot_configure_checks_conditions' guide.\n", "path": "docs/source/user-guide/tabular/tutorials/plot_quick_data_integrity.py"}]}