Fix d4explainer comments format

Author: Boltzmachine

app/projects/d4explainer/page.mdx

@@ -17,7 +17,7 @@ import { Authors, Badges } from '@/components/utils'
 ## Introduction
 Graph neural networks (GNNs) have rapidly gained popularity recently due to their ability to model relational data. However, when it comes to critical decision-making and high-stake applications, such as healthcare, finance, and autonomous systems, the explainability of GNNs is fundamental for humans to understand the model’s decision-making logic and build trust in the deployment of GNNs in real-world scenarios.
 
-<!-- Counterfactual explanation considers  "what-if" scenarios of model predictions, addressing the question of how slight adjustments to the input graph can lead to different model predictions. Model-level explanation, on the other hand, aims to generate the most discriminative graph pattern for a target class, thus shedding light on the overall decision-making behavior and internal functioning of the model.  -->
+{/* Counterfactual explanation considers  "what-if" scenarios of model predictions, addressing the question of how slight adjustments to the input graph can lead to different model predictions. Model-level explanation, on the other hand, aims to generate the most discriminative graph pattern for a target class, thus shedding light on the overall decision-making behavior and internal functioning of the model. */}
 
 ![Figure 1: t-SNE Projection of Tree-Cycle dataset,where Cycle is a counterfactual motif for Tree.|scale=0.5](./assets/intro_fig.png)