Consider a following pipeline:
																			TRUE
File (Source) -> CSV Parser(Transform) -> Filter (Transform) -> Condition1--------> Logistic Regression (Sink) 
																	|		           
															FALSE	| 				   TRUE
																	|-----> Condition2-------> Random Forest (Sink)
																				|
																		FALSE	|
																				|		TRUE
																			Condition3--------> Decision Tree (Sink)
	
In the above pipeline, we want to execute the classification algorithm based on the runtime argument 'input.algorithm'. We also do not want to run the expensive model generation process if the Filter transform did not produce the records enough to proceed further.
 
The pipeline is configured with 3 Condition nodes - 
1. Condition1 node: output.filter Greater Than 1000 && input.algorithm Equals 'Logistic Regression'
2. Condition2 node: output.filter Greater Than 1000 && input.algorithm Equals 'Random Forest'
3. Condition3 node: output.filter Greater Than 1000 && input.algorithm Equals 'Decision Tree'