Arm backend: Correct links in vgf docs (pytorch#21381)

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils
@Sebastian-Larsson @robell @rascani

Signed-off-by: Elena Zhelezina <elena.zhelezina@arm.com>

Author: wwwind

backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in

@@ -44,8 +44,8 @@ See [Partitioner API](arm-vgf-partitioner.md) for more information of the Partit
 
 ## Quantization
 
-The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_ptq.html)
-and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_qat.html).
+The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_ptq.html)
+and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_qat.html).
 
 Partial quantization is supported, allowing users to quantize only specific parts of the model while leaving others in floating-point.
 

docs/source/backends/arm-vgf/arm-vgf-overview.md

@@ -112,8 +112,8 @@ See [Partitioner API](arm-vgf-partitioner.md) for more information of the Partit
 
 ## Quantization
 
-The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_ptq.html)
-and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/tutorials_source/pt2e_quant_qat.html).
+The VGF quantizer supports [Post Training Quantization (PT2E)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_ptq.html)
+and [Quantization-Aware Training (QAT)](https://docs.pytorch.org/ao/main/pt2e_quantization/pt2e_quant_qat.html).
 
 Partial quantization is supported, allowing users to quantize only specific parts of the model while leaving others in floating-point.