# Codeblock 13
class SinusoidalEmbedding(nn.Module):
   def forward(self):
       pos = torch.arange(SEQ_LENGTH).reshape(SEQ_LENGTH, 1)
       print(f"pos\t\t: {pos.shape}")
      
       i = torch.arange(0, EMBED_DIM, 2)
       denominator = torch.pow(10000, i/EMBED_DIM)
       print(f"denominator\t: {denominator.shape}")
      
       even_pos_embed = torch.sin(pos/denominator)  #(1)
       odd_pos_embed  = torch.cos(pos/denominator)  #(2)
       print(f"even_pos_embed\t: {even_pos_embed.shape}")
      
       stacked = torch.stack([even_pos_embed, odd_pos_embed], dim=2)  #(3)
       print(f"stacked\t\t: {stacked.shape}")

       pos_embed = torch.flatten(stacked, start_dim=1, end_dim=2)  #(4)
       print(f"pos_embed\t: {pos_embed.shape}")
      
       return pos_embed