Efficiently add numbers to the end of each dimension of a tensor

Question

I have a tensor x of shape (n, 200). I want to make it shape (n, 218), by appending a tensor of 18 numbers to the end of every "row" of the current tensor. n varies based on the batch size, so I want a way to do this for any n.
As of right now, I have a working solution, but I was wondering if there is a built-in way to do this, I did not see anything in the documentation particularly. My method is:

import torch.nn.functional as F
x = F.pad(input = x, (0, 18, 0, 0)) # pad each tensor in dim 2 with 18 zeroes
for index in range(x.shape[0]): 
    x[index][-18] = nums_to_add # nums_to_add is a tensor with size (1,18)

This works just fine, but I was wondering if there is any easier way to do this, without first padding the zeroes.

Answer 1

torch.cat() is what you are looking for. Here is a snippet:

import torch
a = torch.randint(1,10,(3,4))
b =  torch.randint(1,10,(3,2))
print(a)
print(b)
a = torch.cat((a,b),axis=1) # axis should be one here
print(a)

Output

tensor([[2, 5, 3, 8],
        [3, 9, 5, 3],
        [9, 4, 9, 9]])
tensor([[6, 4],
        [1, 1],
        [8, 3]])
tensor([[2, 5, 3, 8, 6, 4],
        [3, 9, 5, 3, 1, 1],
        [9, 4, 9, 9, 8, 3]])

Now here is a similar example just used repeat to make it same shape in dim=0 so that we can concatenate it easily. (Trying to follow OP's suggestion exactly)

import torch
a = torch.randint(1,10,(5,200)) # shape(5,200)
b =  torch.randint(1,10,(1,18)).repeat((5,1)) # shape(5,18)
a = torch.cat((a,b),axis=1) # axis should be one here
print(a.shape) # (5,218)

The only tricky part of the above solution is the repeat() part (If you can say it complicated ...), which basically repeats this tensor along the specified dimensions. Check here.