I ran a training job for some n sample with a given architecture and save the model using torch.save().
Now, I want to use the saved model (it’s weights) to run a different training job with

exact same architecture
a large number (m samples) of similar (but not same) data
more epocs

However, when I use model = torch.load(saved_model) as my model for re-training the new job, I get inconsistent results i.e. I get different predictions for different run (clear variables, restart kernel) of the new training job.
Sample code can be seen here: How to use pretrained weights for initializing the weights in next iteration? 3