Pytorch dataloader class has the following constructor:
DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,
           batch_sampler=None, num_workers=0, collate_fn=None,
           pin_memory=False, drop_last=False, timeout=0,
           worker_init_fn=None)

When shuffle is set to  True , data is reshuffled at every epoch. Shuffling the order in which examples are fed to the classifier is helpful so that batches between epochs do not look alike. Doing so will eventually make our model more robust.
However, I’m unable to understand  by setting shuffle=True, can we get the same accuracy value for different runs ?