diff --git a/recipes_source/recipes/profiler_recipe.py b/recipes_source/recipes/profiler_recipe.py
index 789f504ffc..a8d1a4dc6b 100644
--- a/recipes_source/recipes/profiler_recipe.py
+++ b/recipes_source/recipes/profiler_recipe.py
@@ -5,31 +5,29 @@
 """
 
 ######################################################################
-"""
-This recipe explains how to use PyTorch profiler and measure the time and
-memory consumption of the model's operators.
-
-Introduction
-------------
-PyTorch includes a simple profiler API that is useful when user needs
-to determine the most expensive operators in the model.
-
-In this recipe, we will use a simple Resnet model to demonstrate how to
-use profiler to analyze model performance.
-
-Prerequisites
----------------
-- ``torch >= 1.9``
-
-Setup
------
-To install ``torch`` and ``torchvision`` use the following command:
-
-.. code-block:: sh
-
-   pip install torch torchvision
-
-"""
+# This recipe explains how to use PyTorch profiler and measure the time and
+# memory consumption of the model's operators.
+#
+# Introduction
+# ------------
+# PyTorch includes a simple profiler API that is useful when the user needs
+# to determine the most expensive operators in the model.
+#
+# In this recipe, we will use a simple Resnet model to demonstrate how to
+# use the profiler to analyze model performance.
+#
+# Prerequisites
+# ---------------
+# - ``torch >= 2.3.0``
+#
+# Setup
+# -----
+# To install ``torch`` and ``torchvision`` use the following command:
+#
+# .. code-block:: sh
+#
+#    pip install torch torchvision
+#
 
 ######################################################################
 # Steps