ImplementClaim probe

Dropout: inverted scaling keeps expectations

Implement `run(x, keep_prob)` returning the inverted-dropout output for input vector x: mask each element with probability p=1-keep_prob (zeros), and scale SURVIVING elements by 1/keep_prob so the expected output equals the input. You may use a fixed deterministic mask for reproducibility: mask[i] = 1 if i % 5 != 0 else 0. The scale factor is 1/keep_prob.

python10 mindifficulty 2/4

From Dropout

Sample tests

run([[1,2,3,4,5,6],0.5])→[0,4,6,8,10,0]
run([[1,2,3,4,5,6],0.8])→[0,2.5,3.75,5,6.25,0]

Hints

Inverted dropout multiplies survivors by 1/(1 - drop_prob) so E[output] = input.
Using a fixed mask keeps the function deterministic and testable.
Output[i] = x[i] * mask[i] * (1/keep_prob) where mask[i] in {0,1}.
Property test: the mean of the output over the mask equals the input mean.

Hints

Code runs in an isolated sandbox on the OpenSandbox judge. No model key required.