where a {\displaystyle \mathbf {a} } is a vector of learnable weights, ⋅ T {\displaystyle \cdot ^{T}} indicates transposition, and LeakyReLU {\displaystyle {\text{LeakyReLU}}} is a modified ReLU activation function. Attention coefficients are normalized to make them easily comparable across different nodes.[7]