Fix links in blog post (#1772)

Signed-off-by: Chris Abraham <[email protected]>

_posts/2024-10-17-pytorch2-5.md

@@ -107,7 +107,7 @@ For more information and examples, please refer to the [official blog post](http
 
 Compiled Autograd is an extension to the PT2 stack allowing the capture of the entire backward pass. Unlike the backward graph traced by AOT dispatcher, Compiled Autograd tracing is deferred until backward execution time, which makes it impervious to forward pass graph breaks, and allows it to record backward hooks into the graph.
 
-Please refer to the [tutorial](https://www.google.com/url?q=https://pytorch.org/tutorials/intermediate/compiled_autograd_tutorial.html&sa=D&source=docs&ust=1728926110018133&usg=AOvVaw3AYnAUHOmsCc0nFy19R6O3) for more information.
+Please refer to the [tutorial](https://pytorch.org/tutorials/intermediate/compiled_autograd_tutorial.html) for more information.
 
 
 ### [Prototype] Flight Recorder
@@ -121,7 +121,7 @@ For more information please refer to the following [tutorial](https://pytorch.or
 
 Max-autotune mode for the Inductor CPU backend in torch.compile profiles multiple implementations of operations at compile time and selects the best-performing one. This is particularly beneficial for GEMM-related operations, using a C++ template-based GEMM implementation as an alternative to the ATen-based approach with oneDNN and MKL libraries. We support FP32, BF16, FP16, and INT8 with epilogue fusions for x86 CPUs. We’ve seen up to 7% geomean speedup on the dynamo benchmark suites and up to 20% boost in next-token latency for LLM inference.
 
-For more information please refer to the [tutorial](https://www.google.com/url?q=https://pytorch.org/tutorials/prototype/max_autotune_on_CPU_tutorial.html&sa=D&source=docs&ust=1728926070319900&usg=AOvVaw27_CteoNRwsbxRlrLy-aEd).
+For more information please refer to the [tutorial](https://pytorch.org/tutorials/prototype/max_autotune_on_CPU_tutorial.html).
 
 
 ### [Prototype] TorchInductor CPU on Windows