No worries, consider following example.
x = torch.randint(10, (3,3))
tensor([[8, 4, 5],
        [3, 0, 3],
        [9, 4, 6]])

consider x is your weight matrix. And the shape of x is torch.Size([3, 3]).
when you try to find max value with x.max(dim=0) where dim is the argument along which you are asking for max value, which in our case is 0 means we are asking for max values from all the columns so it will return
tensor([9, 4, 6])

And when you try to find max value with x.max(dim=1), in this case, dim is 1 means we are asking for max values across all the rows so it will return
tensor([8, 3, 9])

So this difference i was trying to convay by



 yash1994:

You need to determine dimension along which you need the max value