update bug_report model links and notebook (#10665)

Author: rohitgr7

.github/ISSUE_TEMPLATE/bug_report.md

@@ -16,11 +16,11 @@ assignees: ''
 Please reproduce using the BoringModel!
 
 You can use the following Colab link:
-https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report/The_BoringModel.ipynb
+https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report/bug_report_model.ipynb
 IMPORTANT: has to be public.
 
 or this simple template:
-https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report_model.py
+https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report/bug_report_model.py
 
 If you could not reproduce using the BoringModel and still think there's a bug, please post here
 but remember, bugs with code are fixed faster!
@@ -46,9 +46,9 @@ python collect_env_details.py
 You can also fill out the list below manually.
 -->
 
-- PyTorch Lightning Version (e.g., 1.3.0):
-- PyTorch Version (e.g., 1.8)
-- Python version:
+- PyTorch Lightning Version (e.g., 1.5.0):
+- PyTorch Version (e.g., 1.10):
+- Python version (e.g., 3.9):
 - OS (e.g., Linux):
 - CUDA/cuDNN version:
 - GPU models and configuration:

docs/source/advanced/fault_tolerant_training.rst

@@ -134,7 +134,7 @@ Performance Impacts
 -------------------
 
 Fault-tolerant Training was tested on common and worst-case scenarios in order to measure the impact of the internal state tracking on the total training time.
-On tiny models like the `BoringModel and RandomDataset <https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report_model.py>`_
+On tiny models like the `BoringModel and RandomDataset <https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report/bug_report_model.py>`_
 which has virtually no data loading and processing overhead, we noticed up to 50% longer training time with fault tolerance enabled.
 In this worst-case scenario, fault-tolerant adds an overhead that is noticeable in comparison to the compute time for dataloading itself.
 However, for more realistic training workloads where data loading and preprocessing is more expensive, the constant overhead that fault tolerance adds becomes less noticeable or not noticeable at all.