diff --git a/recipes_source/torch_logs.py b/recipes_source/torch_logs.py
index b5c3f0bd8a..45ca17584f 100644
--- a/recipes_source/torch_logs.py
+++ b/recipes_source/torch_logs.py
@@ -3,9 +3,7 @@
 ==========================================================================================
 **Author:** `Michael Lazos <https://github.com/mlazos>`_
 """
-
 import logging
-
 ######################################################################
 #
 # This tutorial introduces the ``TORCH_LOGS`` environment variable, as well as the Python API, and
@@ -16,8 +14,6 @@
 #   This tutorial requires PyTorch 2.2.0 or later.
 #
 #
-
-
 ######################################################################
 # Setup
 # ~~~~~~~~~~~~~~~~~~~~~
@@ -29,10 +25,22 @@
 #   There is also an environment variable ``TORCH_LOGS``, which can be used to
 #   change logging settings at the command line. The equivalent environment
 #   variable setting is shown for each example.
-
 import torch
 
-# exit cleanly if we are on a device that doesn't support torch.compile
+######################################################################
+# Device Compatibility Check
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The following code checks if the device supports torch.compile, which requires
+# CUDA compute capability 7.0 or higher.
+#
+# .. important::
+#
+#   If your device does not meet this requirement, this tutorial will only print
+#   a skip message and exit. You will NOT see any of the logging output examples
+#   (Dynamo Tracing, Traced Graph, Fusion Decisions, Output Code) demonstrated
+#   below. To see the actual logging output, you need a CUDA-capable GPU with
+#   compute capability 7.0 or higher (e.g., V100, T4, or newer).
+#
 if torch.cuda.get_device_capability() < (7, 0):
     print("Skipping because torch.compile is not supported on this device.")
 else:
@@ -40,44 +48,33 @@
     def fn(x, y):
         z = x + y
         return z + 2
-
-
     inputs = (torch.ones(2, 2, device="cuda"), torch.zeros(2, 2, device="cuda"))
-
-
 # print separator and reset dynamo
 # between each example
     def separator(name):
         print(f"==================={name}=========================")
         torch._dynamo.reset()
-
-
     separator("Dynamo Tracing")
 # View dynamo tracing
 # TORCH_LOGS="+dynamo"
     torch._logging.set_logs(dynamo=logging.DEBUG)
     fn(*inputs)
-
     separator("Traced Graph")
 # View traced graph
 # TORCH_LOGS="graph"
     torch._logging.set_logs(graph=True)
     fn(*inputs)
-
     separator("Fusion Decisions")
 # View fusion decisions
 # TORCH_LOGS="fusion"
     torch._logging.set_logs(fusion=True)
     fn(*inputs)
-
     separator("Output Code")
 # View output code generated by inductor
 # TORCH_LOGS="output_code"
     torch._logging.set_logs(output_code=True)
     fn(*inputs)
-
     separator("")
-
 ######################################################################
 # Conclusion
 # ~~~~~~~~~~