From 0e7bf665f157b8d04222c15be591aa1e930d61f5 Mon Sep 17 00:00:00 2001
From: MRIDUL JAIN <105979087+Spinachboul@users.noreply.github.com>
Date: Sun, 14 Jan 2024 07:13:11 +0530
Subject: [PATCH] Update tensor_prod.md

---
 docs/src/tensor_prod.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/src/tensor_prod.md b/docs/src/tensor_prod.md
index 015aef85..27b395a2 100644
--- a/docs/src/tensor_prod.md
+++ b/docs/src/tensor_prod.md
@@ -9,7 +9,7 @@ a: A constant parameter
 
 # Generating Data and Plotting
 
-```
+```@example
 function tensor_product_function(x, a)
     return prod(cos.(a * π * xi) for xi in x)
 end
@@ -46,7 +46,7 @@ plot_data_and_true_function(x_train, y_train, x_test, y_test, a, lb, ub)
 # Training various Surrogates
 Now let's train various surrogate models and evaluate their performance on the test data
 
-```
+```@example
 # Train different surrogate models
 function train_surrogates(x_train, y_train, lb, ub, alpha=2.0, n=6)
     loba = LobachevskySurrogate(x_train, y_train, lb, ub, alpha=alpha, n=n)
@@ -82,7 +82,7 @@ plot_surrogate_predictions(loba_pred, krig_pred, x_test, y_test, 2.0, lb, ub)
 # Reporting the best Surrogate Model
 To determine the best surrogate, you can compare their accuracy and performance metrics on the test data. For instance, you can calculate and compare the mean squared error (MSE) or any other relevant metric
 
-```
+```@example
 using Statistics
 
 # Evaluate performance metrics