Fix range footnote

Co-authored-by: Jacob Sznajdman <breakanalysis@gmail.com>

Author: FlorentinD

doc/asciidoc/pythonclient/python-client-pipelines.adoc

@@ -42,10 +42,10 @@ Below is a description of the methods on such objects:
                             Union[str, list[str]]  | Series                  | <<nodeclassification-pipelines-adding-features, Select node properties to be used as features>>.
 | configureSplit        | config: **kwargs         | Series                  | <<nodeclassification-pipelines-configure-splits, Configure the train-test dataset split>>.
 | addLogisticRegression | parameter_space: +
-                            dict[str, any]         | Series                  | <<nodeclassification-pipelines-adding-model-candidates, Add a logistic regression model configuration to train as a candidate in the model selection phase>>.
+                            dict[str, any]         | Series                  | <<nodeclassification-pipelines-adding-model-candidates, Add a logistic regression model configuration to train as a candidate in the model selection phase>>. footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
 | addRandomForest       | parameter_space: +
-                            dict[str, any]         | Series                  | <<nodeclassification-pipelines-adding-model-candidates, Add a random forest model configuration to train as a candidate in the model selection phase>>.
-| configureAutoTuning   | config: **kwargs footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
+                            dict[str, any]         | Series                  | <<nodeclassification-pipelines-adding-model-candidates, Add a random forest model configuration to train as a candidate in the model selection phase>>. footnote:range[]
+| configureAutoTuning   | config: **kwargs
                                                    | Series                  | <<nodeclassification-pipelines-configure-auto-tuning, Configure the auto-tuning>>.
 | train                 | G: Graph, +
                             config: **kwargs       | NCPredictionPipeline, +
@@ -190,10 +190,10 @@ Below is a description of the methods on such objects:
                         config: **kwargs         | Series              | <<linkprediction-adding-features, Add a link feature for model training based on node properties and a feature combiner>>.
 | configureSplit        | config: **kwargs       | Series              | <<linkprediction-configure-splits, Configure the feature-train-test dataset split>>.
 | addLogisticRegression | parameter_space: +
-                          dict[str, any]         | Series              | <<linkprediction-adding-model-candidates, Add a logistic regression model configuration to train as a candidate in the model selection phase>>.
+                          dict[str, any]         | Series              | <<linkprediction-adding-model-candidates, Add a logistic regression model configuration to train as a candidate in the model selection phase>>. footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
 | addRandomForest       | parameter_space: +
-                          dict[str, any]         | Series              | <<linkprediction-adding-model-candidates, Add a random forest model configuration to train as a candidate in the model selection phase>>.
-| configureAutoTuning   | config: **kwargs footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
+                          dict[str, any]         | Series              | <<linkprediction-adding-model-candidates, Add a random forest model configuration to train as a candidate in the model selection phase>>. footnote:range[]
+| configureAutoTuning   | config: **kwargs
                                                  | Series              | <<linkprediction-configure-auto-tuning, Configure the auto-tuning>>.
 | train                 | G: Graph, +
 config: **kwargs        | LPPredictionPipeline, +
@@ -334,10 +334,10 @@ config: **kwargs       | Series                  | <<noderegression-pipelines-ad
 Union[str, list[str]]  | Series                  | <<noderegression-pipelines-adding-features, Select node properties to be used as features>>.
 | configureSplit        | config: **kwargs         | Series                  | <<noderegression-pipelines-configure-splits, Configure the train-test dataset split>>.
 | addLinearRegression | parameter_space: +
-dict[str, any]         | Series                  | <<noderegression-pipelines-adding-model-candidates, Add a linear regression model configuration to train as a candidate in the model selection phase>>.
+dict[str, any]         | Series                  | <<noderegression-pipelines-adding-model-candidates, Add a linear regression model configuration to train as a candidate in the model selection phase>>. footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
 | addRandomForest       | parameter_space: +
-dict[str, any]         | Series                  | <<noderegression-pipelines-adding-model-candidates, Add a random forest model configuration to train as a candidate in the model selection phase>>.
-| configureAutoTuning   | config: **kwargs footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
+dict[str, any]         | Series                  | <<noderegression-pipelines-adding-model-candidates, Add a random forest model configuration to train as a candidate in the model selection phase>>. footnote:range[]
+| configureAutoTuning   | config: **kwargs
 | Series                  | <<noderegression-pipelines-configure-auto-tuning, Configure the auto-tuning>>.
 | train                 | G: Graph, +
 config: **kwargs       | NCPredictionPipeline, +