Merge branch 'issues/update_torchserve_version' of https://github.com/agunapal/pytorch.github.io into issues/update_torchserve_version

Author: agunapal

_posts/2023-10-04-new-library-updates.md

@@ -46,7 +46,7 @@ Please refer to <https://pytorch.org/audio/2.1/tutorials/squim_tutorial.html> fo
 
 \`torchaudio.models.decoder.CUCTCDecoder\` performs CTC beam search in CUDA devices. The beam search is fast. It eliminates the need to move data from CUDA device to CPU when performing automatic speech recognition. With PyTorch's CUDA support, it is now possible to perform the entire speech recognition pipeline in CUDA.
 
-Please refer to <https://pytorch.org/audio/master/tutorials/asr_inference_with_cuda_ctc_decoder_tutorial.html> for the detail.
+Please refer to <https://pytorch.org/audio/2.1/tutorials/asr_inference_with_cuda_ctc_decoder_tutorial.html> for the detail.
 
 **\[Prototype] Utilities for AI music generation**
 
@@ -77,7 +77,7 @@ Please refer to <https://pytorch.org/audio/2.1/installation.html#optional-depend
 
 TorchAudio now depends on libsox installed separately from torchaudio. Sox I/O backend no longer supports file-like objects. (This is supported by FFmpeg backend and soundfile.)
 
-Please refer to <https://pytorch.org/audio/master/installation.html#optional-dependencies> for the details.
+Please refer to <https://pytorch.org/audio/2.1/installation.html#optional-dependencies> for the details.
 
 ## TorchRL
 

_posts/2023-10-04-pytorch-2-1.md

@@ -90,7 +90,7 @@ For more information, please see the tutorial [here](https://pytorch.org/tutoria
 
 **\[Prototype] _torch.export_-based Quantization**
 
-_torch.ao.quantization_ now supports post-training static quantization on PyTorch2-based _torch.export_ flows.  This includes support for built-in _XNNPACK_ and _X64Inductor_ _Quantizer_, as well as the ability to specify one’s own _Quantizer_.
+_torch.ao.quantization_ now supports quantization on PyTorch2-based _torch.export_ flows.  This includes support for built-in _XNNPACK_ and _X64Inductor_ _Quantizer_, as well as the ability to specify one’s own _Quantizer_.
 
 For an explanation on post-training static quantization with torch.export, see [this tutorial](https://pytorch.org/tutorials/prototype/pt2e_quant_ptq.html), for quantization-aware training for static quantization with torch.export, see [this tutorial](https://pytorch.org/tutorials/prototype/pt2e_quant_qat.html).