replace v2.4.2 links to main in troubleshoot docs

Author: ueno-hiroshi002

docsrc/source_troubleshoot/index.rst

@@ -48,6 +48,6 @@ If quantization accuracy of your model does not improve after reading Judgeable
 
 References
 ============================================
-[1] `Quantization Troubleshooting for MCT <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/v2.4.2/quantization_troubleshooting.md>`_
+[1] `Quantization Troubleshooting for MCT <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/main/quantization_troubleshooting.md>`_
 
 [2] `PyTorch documentation (v2.5) <https://docs.pytorch.org/docs/2.5/index.html>`_

docsrc/source_troubleshoot/troubleshoots/enabling_hessian-based_mixed_precision.rst

@@ -8,7 +8,7 @@ Enabling Hessian-based Mixed Precision
 ============================================
 In Mixed Precision quantization, MCT will assign a different bit width to each weight in the model, depending on the weight's layer sensitivity and a resource constraint defined by the user, such as target model size.
 
-Check out the `Mixed Precision tutorial <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/v2.4.2/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mixed_precision_ptq.ipynb>`_ for more information.
+Check out the `Mixed Precision tutorial <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mixed_precision_ptq.ipynb>`_ for more information.
 
 Overview
 ==============================

docsrc/source_troubleshoot/troubleshoots/gptq-gradient_based_post_training_quantization.rst

@@ -13,7 +13,7 @@ When PTQ (either with or without Mixed Precision) fails to deliver the required
 
 In GPTQ, MCT will finetune the model's weights and quantization parameters for improved accuracy. The finetuning process will only use the label-less representative dataset.
 
-Check out the `GPTQ tutorial <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/v2.4.2/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mobilenet_gptq.ipynb>`_ for more information and an implementation example.
+Check out the `GPTQ tutorial <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mobilenet_gptq.ipynb>`_ for more information and an implementation example.
 
 Solution
 =================================

docsrc/source_troubleshoot/troubleshoots/mixed_precision_with_model_output_loss_objective.rst

@@ -8,7 +8,7 @@ Mixed Precision with model output loss objective
 =================================================
 In Mixed Precision quantization, MCT will assign a different bitwidth to each weight in the model, depending on the weight's layer sensitivity and a resource constraint defined by the user, such as target model size.
 
-Check out the `Mixed Precision tutorial <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/v2.4.2/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mixed_precision_ptq.ipynb>`_ for more information.
+Check out the `Mixed Precision tutorial <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mixed_precision_ptq.ipynb>`_ for more information.
 
 Overview
 ==============================

docsrc/source_troubleshoot/troubleshoots/shift_negative_activation.rst

@@ -48,4 +48,4 @@ Set ``shift_negative_activation_correction`` to True in the ``QuantizationConfig
 .. note::
 
   After activating this flag, you have a few more tweaks to its operation that you can control with the ``shift_negative_ratio``, ``shift_negative_threshold_recalculation`` & ``shift_negative_params_search`` flags. 
-  Read all about them in the `quantization configuration <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/v2.4.2/model_compression_toolkit/core/common/quantization/quantization_config.py#L97-L99>`_ class description.
+  Read all about them in the `quantization configuration <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/main/model_compression_toolkit/core/common/quantization/quantization_config.py>`_ class description.

docsrc/source_troubleshoot/troubleshoots/using_more_samples_in_mixed_precision_quantization.rst

@@ -8,7 +8,7 @@ Using more samples in Mixed Precision quantization
 ===================================================
 In Mixed Precision quantization, MCT will assign a different bit width to each weight in the model, depending on the weight's layer sensitivity and a resource constraint defined by the user, such as target model size.
 
-Check out the `mixed precision tutorial <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/v2.4.2/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mixed_precision_ptq.ipynb>`_ for more information.
+Check out the `mixed precision tutorial <https://github.com/SonySemiconductorSolutions/mct-model-optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_mixed_precision_ptq.ipynb>`_ for more information.
 
 Overview
 ==============================