We want to find the self-attention weights assigned to the tokens in the sequence “Attention is everything” using scaled dot product attention. A single head is used. The sequence is of length 3, and the dimensionality of the transformer is 4. Below is the input embedding of shape (3, 4). Note that this embedding is the sum of the token embedding and the position embedding.   X = [1, 2, 3, 4]        [5, 0, 7, 0]        [9, 0, 1, 2]   The weights of the Q, K, and V are:   Wq = [0.3, 0.2, 0.8, 0.9]           [0.4, 0.1, 0.4, 0.5]           [0.5, 0.7, 0.2, 0.8]           [0.8, 0.8, 0.7, 0.4]   Wk = [0.3, 0.9, 0.2, 0.7]           [0.5, 0.4, 0.2, 0.2]           [0.1, 0.7, 0.3, 0.6]           [0.8, 0.4, 0.5, 0.9]   Wv = [0.2, 0.2, 0.3, 0.9]           [0.2, 0.3, 0.8, 0.6]           [0.7, 0.5, 0.9, 0.9]           [1.0, 0.4, 0.2, 0.5]   If a causal mask is applied, what attention weight does “is” assign to “everything” in the sequence “Attention is everything”? Give the answer to 2dp. Hint: Lecture 19 slides 17 - 27