Fix inconsistencies in fgsm_tutorial

[QasimKhan5x]

Fixes #1032

Description

1. The architecture of the model is changed. The tutorial says that it uses the same MNIST model from pytorch/examples/mnist. However, the dimensions of the convolutional layers were different. Therefore, the exact model is copy-pasted as is.
2. The model in the MNIST example was trained using a transform in the data loader that normalized the data using mean=0.1307 and std=0.3081. However, no normalization was being applied in this example. Thus, the same normalization is applied to the test_loader in this example.
3. The state_dict of the MNIST was being loaded on the CPU even though use_cuda=True and device is being dynamically defined. Therefore, map_location=device instead of cpu
4. A denormalization function is added to unnormalize the data before applying FGSM on it, since FGSM takes unnormalized input
5. The test function is modified. After computing the gradient of the normalized data, the data is unnormalized. FGSM is then applied on unnormalized data. Finally, the perturbed data is again normalized before applying the model on it.

I also checked the code without step 5, i.e., not performing normalization and denormalization inside the test function. The observation was that the accuracy stayed the same between 98-99%, although it was supposed to fall. Applying step 5 rectified this issue.

Checklist

• The issue that is being fixed is referred in the description (see above "Fixes #ISSUE_NUMBER")
• Only one issue is addressed in this pull request
• Labels from the issue that this PR is fixing are added to this pull request
• No unnecessary issues are included into this pull request.

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