Unexpected broadcasting behavior with np.add on non-aligned array shapes in NumPy 1.25
I've searched everywhere and can't find a clear answer. I ran into an scenario while trying to perform element-wise addition on two NumPy arrays using `np.add`. I have two arrays, `A` and `B`, where `A` is shaped (3, 4) and `B` has a shape of (4,). My expectation was that `B` would be broadcasted across each row of `A`. However, I received a ValueError when executing the addition. Here's the code I'm using: ```python import numpy as np A = np.random.rand(3, 4) # Shape (3, 4) B = np.random.rand(4) # Shape (4,) try: result = np.add(A, B) except ValueError as e: print(e) ``` The behavior message states: `ValueError: shapes (3,4) and (4,) not aligned: 4 (dim 1) != 3 (dim 0)`. I thought that since `B` has a compatible shape for broadcasting with `A`, it should work. I also confirmed that `B` is a 1D array which should align with the second dimension of a 2D array. I've checked the compatibility rules for broadcasting in the NumPy documentation and it seems like I followed them correctly. I also tried reshaping `B` to (1, 4) and (3, 4), but I still receive the same behavior. Is there something wrong with my approach, or do I need to reshape `B` in a different way to achieve the desired addition? This is part of a larger service I'm building.