PyTorch

These are the few tensor functions I've choosen.

For more functions visit:
https://pytorch.org/docs/stable/torch.html

torch.cat
torch.unsqueeze
torch.view
torch.zeros_like
torch.arange

# Import torch and other required modules
import torch

Function 1 - torch.cat

torch.cat is a built-in function which can concatenate sequences of given tensor in the given dimension

# here are two 2 x 2 tensors (x and y)
# manual tensors.
tensor_x = torch.tensor([[5, 6], 
                         [8, 7]])

tensor_y = torch.tensor([[10, 11], 
                         [12, 13]])

# concatenate tensor in the given dim (0 - rows, 1 - column)
# concat on dim = 0 (rows)
vec_row = torch.cat((tensor_x, tensor_y), dim=0)
# concat on dim = 1 (columns)
vec_col = torch.cat((tensor_x, tensor_y), dim=1)

print(vec_row, vec_col, vec_row.shape, vec_col.shape, sep='\n\n',)

tensor([[ 5,  6],
        [ 8,  7],
        [10, 11],
        [12, 13]])

tensor([[ 5,  6, 10, 11],
        [ 8,  7, 12, 13]])

torch.Size([4, 2])

torch.Size([2, 4])

Not only two tensors, torch.cat can concatenate n-number of tensor in the given dimension.