When evaluating a dropout layer implementation, its behavior varies predictably at boundary probabilities. Applying a dropout operation with a probability of $0$ leaves the input completely unchanged, as no elements are dropped. Using a dropout probability of $0.5$ drops approximately half of the elements and rescales the surviving ones by a factor of $2$. Conversely, applying a dropout probability of $1$ drops all elements entirely, resulting in a tensor of zeros.