[Doctests] Correct task summary (#16644)

docs/source/en/task_summary.mdx

@@ -1090,16 +1090,15 @@ The following examples demonstrate how to use a [`pipeline`] and a model and tok
 >>> from transformers import pipeline
 
 >>> vision_classifier = pipeline(task="image-classification")
->>> vision_classifier(
+>>> result = vision_classifier(
 ...     images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
 ... )
-[{'label': 'lynx, catamount', 'score': 0.4403027892112732},
- {'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
-  'score': 0.03433405980467796},
- {'label': 'snow leopard, ounce, Panthera uncia',
-  'score': 0.032148055732250214},
- {'label': 'Egyptian cat', 'score': 0.02353910356760025},
- {'label': 'tiger cat', 'score': 0.023034192621707916}]
+>>> print("\n".join([f"Class {d['label']} with score {round(d['score'], 4)}" for d in result]))
+Class lynx, catamount with score 0.4335                                                    
+Class cougar, puma, catamount, mountain lion, painter, panther, Felis concolor with score 0.0348
+Class snow leopard, ounce, Panthera uncia with score 0.0324          
+Class Egyptian cat with score 0.0239                                                       
+Class tiger cat with score 0.0229
 ```
 
 The general process for using a model and feature extractor for image classification is: