I know its really simple but I am new to NN and I am having a lot of difficulties understanding the relation between the math and the PyTorch code. I am trying to replicate a paper that uses attention weights and I need to implement this feed forward neural network with two inputs.
$$ c_i = W_1 tanh(W_2m_i + W_3v_a + b_i$$

$m_i$ is a vector of embeddings of a single word 1 for each in the sentence and $v_a$ is the embedding vector of
The model parameters are: $$W_1 \in R^{1xd}, W_2 \in R^{dxd}, W_3 \in R^{dxd}, b_1 \in {}$$
The resulting  ${c_1, c_2,..., c_N}$  after being passed through a softmax will represent the weight that is given to each word in the sentence