feat: Add VJP support for numpy.take function

[SIVALANAGASHANKARNIVAS]

Implements gradient computation for numpy.take function.

• Adds untake_along_axis primitive for scattering gradients back
• Handles both axis=None (flattened) and specific axis cases
• Uses numpy.add.at for proper gradient accumulation with repeated indices

Fixes #743

Changes Made

This PR adds VJP (Vector-Jacobian Product) support for numpy.take, enabling gradient computation through this function.

Implementation Details

• Created untake_along_axis primitive that scatters gradients back to original array positions
• The VJP handles both axis=None (flattened array) and specific axis cases
• Uses numpy.add.at for proper gradient accumulation when indices are repeated

Testing

With this change, the following code now works:

import autograd.numpy as anp
import autograd as ag

x = anp.array([[1., 2., 3.], [4., 5., 6.]])
idx = anp.array([0, 2])

def foo(x):
    return anp.take(x, idx, axis=1).sum()

grad_fn = ag.grad(foo)
print(grad_fn(x))  # Now works!

[agriyakhetarpal]

Thanks @SIVALANAGASHANKARNIVAS – could you please add a few tests? 🙏🏻