I have 50 respondents x 120 items and ratings given by them. I train my model with 3 for-loops:
for each epoch:
 for each respondent of 50
   for each item of 120
      # the label is rating of respondent i item j.
      # the label dimension would be [1, 1]

I was wondering if I can train the model like the following way:
for each epoch:
  for each item of 120:
   # the labels are ratings of 50 respondents item j.
   # the label dimension would be [50, 1]

Can pytorch realize this and optimize the neural network model? However, in this case, I am not sure how the model parameters will be updated.