Out of Bounds Index Handling

[Impure-King]

Recently, I was relearn ml and decided to pick JAX. However, a peculiarity I noticed was in the following code:

import jax
import jax.numpy as jnp
import pandas as pd
import numpy as np

# Creating a 3 x 2 matrix:
M = jnp.array([[1, 2],
               [3, 4],
               [5, 6]])
print(M.shape)
print(M[3][2]) # Should raise an error, but instead returns 6

Is this an intended behavior? Wouldn't this lead to implicit errors in one's code?

[Impure-King]

I am running the code on JAX cuda 12 version 0.5.3 build on my RTX 3070.

[Impure-King]

Nevermind, I did some searching and found out the reasoning at https://stackoverflow.com/questions/75770847/jax-vs-numpy-array-indexing-out-of-bounds-behaviour. It is also in the JAX sharp bits for anyone interested in the future.