Transformer encoder block is identical to the decoder part of the original model . It consists of a stack of identical layers incorporating masked multi-head self-attention pooling with residual connection, layer normalization , and position-wise feed-forward network with residual connection. Self-attention sublayer preserves autoregressive property by masking out future tokens and independently tries to attend different fragments with multiple heads. It is then followed by a normalization across the embedding features. The obtained representations at all the time steps are processed using the same feed-forward network and then also followed by layer normalization. The computational complexity is O(τ 2 d) with constant minimum number of sequential operations. Optionally, dropouts are applied to the attention weights and/or the output of the multi-head attention.