To implement dropout for a layer computationally, we must draw samples from a Bernoulli distribution for each dimension, where a node is kept (value $1$) with probability $1-p$ and dropped (value $0$) with probability $p$. An efficient way to achieve this is to generate a tensor of samples from a uniform distribution $U[0, 1]$ and apply a threshold, keeping only those elements where the uniformly sampled value is strictly greater than the dropout probability $p$.