Small update to modes on torch.compile tutorial and explain why 2nd run in slower

intermediate_source/torch_compile_tutorial.py

@@ -195,11 +195,15 @@ def init_model():
 # GPU compute and the observed speedup may be less significant.
 #
 # You may also see different speedup results depending on the chosen ``mode``
-# argument. Since our model and data are small, we want to reduce overhead as
-# much as possible, and so we chose ``"reduce-overhead"``. For your own models,
+# argument. The ``"reduce-overhead"`` mode uses CUDA graphs to further reduce
+# the overhead of Python. For your own models,
 # you may need to experiment with different modes to maximize speedup. You can
 # read more about modes `here <https://pytorch.org/get-started/pytorch-2.0/#user-experience>`__.
 #
+# You may might also notice that the second time we run our model with ``torch.compile`` is significantly
+# slower than the other runs, although it is much faster than the first run. This is because the ``"reduce-overhead"``
+# mode runs a few warm-up iterations for CUDA graphs.
+#
 # For general PyTorch benchmarking, you can try using ``torch.utils.benchmark`` instead of the ``timed``
 # function we defined above. We wrote our own timing function in this tutorial to show
 # ``torch.compile``'s compilation latency.