Need help implementing the generate$\_$kFold function, which takes in the number of training examples n and the number of folds k and returns a list of k folds, where each fold takes the form $($training indices, validation indices$).$

For instance, if n $=3$ and k $=3,$ then we have three indices $[0,1,2]$ and we are trying to split it k $=3$ times to obtain different training$/$validation splits. One possible output of the the function is $[([0,1],[2]),([1,2],[0]),([0,2],[1])].$ It is possible that k might not divide n with remainder $0.$ In that case, you can divide n k$-1$ times fully and have the remainder constitute the final fold. For instance, if n $=5$ and k $=4,$ one possible output is $[([1,2,3,4],[0]),([0,2,3,4],[1]),([0,1,3,4],[2]),([0,1,2],[3,4])].$

Ensure that no two folds have the same indices as it is wasteful to train a model on the same training$/$validation split again.

One possible algorithm: divide the list of n indices into k parts and loop k times, collecting all but $1$ parts into the training set for Please hthat fold and leaving that part as the validation set.

Please help me replace # YOUR CODE HERE

def generate$\_$kFold$($n$,$ k$)$:

$"""$

Generates $[($training$\_$indices, validation$\_$indices$),...]$ for k$-$fold validation.

Input:

n: number of training examples

k: number of folds

Output:

kfold$\_$indices: a list of length k$.$ Each entry takes the form $($training indices, validation indices$)$

$"""$

assert k $>=2$

kfold$\_$indices $=[]$

# YOUR CODE HERE

raise NotImplementedError$()$

return kfold$\_$indices