Regenerate Tutorials from notebook sources

Co-authored-by: Florentin Dörre <florentin.dorre@neotechnology.com>

Author: Mats-SX

doc/modules/ROOT/pages/tutorials/community-detection.adoc

@@ -29,7 +29,7 @@ connection to the database.
 [source, python, role=no-test]
 ----
 # Install necessary dependencies
-%pip graphdatascience pandas
+%pip install graphdatascience pandas
 ----
 
 [source, python, role=no-test]

doc/modules/ROOT/pages/tutorials/fastrp-and-knn.adoc

@@ -49,7 +49,7 @@ our GDS client connection to the database.
 [source, python, role=no-test]
 ----
 # Install necessary dependencies
-%pip graphdatascience
+%pip install graphdatascience
 ----
 
 [source, python, role=no-test]

doc/modules/ROOT/pages/tutorials/gds-sessions.adoc

@@ -28,7 +28,7 @@ to have the `graphdatascience` Python library installed, version
 
 [source, python, role=no-test]
 ----
-%pip install graphdatascience==1.10a1
+%pip install --pre "graphdatascience>1.9"
 ----
 
 == Aura API credentials
@@ -298,6 +298,12 @@ sessions.delete("people-and-fruits")
 sessions.list()
 ----
 
+[source, python, role=no-test]
+----
+# Lastly, let's clean up the database
+gds.run_cypher("MATCH (n:Person|Fruit) DETACH DELETE n")
+----
+
 == Conclusion
 
 And we’re done! We have created a GDS Session, projected a graph, run

doc/modules/ROOT/pages/tutorials/import-sample-export-gnn.adoc

@@ -51,7 +51,7 @@ connection to the database.
 [source, python, role=no-test]
 ----
 # Install necessary dependencies
-%pip install torch torch_scatter torch_sparse torch_geometric graphdatascience
+%pip install graphdatascience torch torch_scatter torch_sparse torch_geometric
 ----
 
 [source, python, role=no-test]