Below is a quicker way to get this done.  I have considered a binary classification problem. This can be easily extended to a multi class classification problem.
import numpy as np
import torch
data = torch.randn(64,224,224)
target = torch.from_numpy(np.random.randint(0,1,64))
predicted = torch.from_numpy(np.random.randn(64,2))
pred = predicted.argmax(dim = 1) ## Note that keepdim is false
### This line would do the trick of obtaining incorrect data
incorrect_data = data[pred!=target,:,:]