Repository: deepchecks/deepchecks

Issue:
[BUG] Docs for Simple Classification Data tutorial load with missing plots
**Describe the bug**
The plots in the doc page for the [Simple Classification Data tutorial](https://docs.deepchecks.com/stable/auto_tutorials/vision/plot_simple_classification_tutorial.html#sphx-glr-auto-tutorials-vision-plot-simple-classification-tutorial-py) load partially. Some times and for some machines the suite loads, and sometime it doesn't the second plot doesn't load at all

**To Reproduce**
Open https://docs.deepchecks.com/stable/auto_tutorials/vision/plot_simple_classification_tutorial.html#sphx-glr-auto-tutorials-vision-plot-simple-classification-tutorial-py

**Screenshots**
![image](https://user-images.githubusercontent.com/17730502/166140433-9c963589-110f-4243-82c2-aba736e89c3b.png)



Discussion:

Resolving patch:
diff --git a/docs/source/tutorials/vision/plot_classification_tutorial.py b/docs/source/tutorials/vision/plot_classification_tutorial.py
--- a/docs/source/tutorials/vision/plot_classification_tutorial.py
+++ b/docs/source/tutorials/vision/plot_classification_tutorial.py
@@ -195,7 +195,6 @@ def imshow(inp, title=None):
 print("Image values", batch[1][0])
 
 #%%
-# And we can watch the output:
 # Implementing the ClassificationData class
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 # The first step is to implement a class that enables deepchecks to interact with your model and data.
diff --git a/docs/source/tutorials/vision/plot_detection_tutorial.py b/docs/source/tutorials/vision/plot_detection_tutorial.py
--- a/docs/source/tutorials/vision/plot_detection_tutorial.py
+++ b/docs/source/tutorials/vision/plot_detection_tutorial.py
@@ -224,8 +224,6 @@ def show(imgs):
 print("Second element is: ", type(batch[1]), "with len of ", len(batch[1]))
 print("Example output of a label shape from the dataloader ", batch[1][0])
 
-# And we can watch the output:
-
 #%%
 # Implementing the DetectionData class
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/source/tutorials/vision/plot_simple_classification_tutorial.py b/docs/source/tutorials/vision/plot_simple_classification_tutorial.py
--- a/docs/source/tutorials/vision/plot_simple_classification_tutorial.py
+++ b/docs/source/tutorials/vision/plot_simple_classification_tutorial.py
@@ -22,7 +22,7 @@
 # For this example we'll use a small sample of the RGB `EuroSAT dataset <https://github.com/phelber/eurosat#>`_.
 # EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes
 # with 27000 labeled and geo-referenced samples.
-
+#
 # Citations:
 #
 # [1] Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. Patrick
@@ -99,7 +99,7 @@
 #
 # The second failure is more interesting. The :doc:`Simple Feature Contribution
 # </checks_gallery/vision/methodology/plot_simple_feature_contribution>` check computes various :doc:`simple
-# image # properties </user-guide/vision/vision_properties>` and checks if the image label can be inferred using a
+# image properties </user-guide/vision/vision_properties>` and checks if the image label can be inferred using a
 # simple model (for example, a Classification Tree) using the property values. The ability to predict the label using
 # these properties is measures by the Predictive Power Score (PPS) and this measure is compared between the training
 # and test dataset. In this case, the condition alerts us to the fact that this PPS for the "RMS Contrast" property was
@@ -109,7 +109,7 @@
 
 check_idx = np.where([result.results[i].check.name() == 'Simple Feature Contribution'
                       for i in range(len(result.results))])[0][0]
-result.results[check_idx].display[0]
+result.results[check_idx]
 
 #%%
 # Here we can see the plot dedicated to the PPS of the property RMS Contrast, which measures the contrast in the


Test patch: