diff --git a/ML/Pytorch/more_advanced/transformer_from_scratch/transformer_from_scratch.py b/ML/Pytorch/more_advanced/transformer_from_scratch/transformer_from_scratch.py
index 80396df6..9ca26442 100644
--- a/ML/Pytorch/more_advanced/transformer_from_scratch/transformer_from_scratch.py
+++ b/ML/Pytorch/more_advanced/transformer_from_scratch/transformer_from_scratch.py
@@ -103,17 +103,7 @@ def forward(self, value, key, query, mask):
 
 
 class Encoder(nn.Module):
-    def __init__(
-        self,
-        src_vocab_size,
-        embed_size,
-        num_layers,
-        heads,
-        device,
-        forward_expansion,
-        dropout,
-        max_length,
-    ):
+    def __init__(self,src_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length,):
 
         super(Encoder, self).__init__()
         self.embed_size = embed_size
@@ -122,25 +112,15 @@ def __init__(
         self.position_embedding = nn.Embedding(max_length, embed_size)
 
         self.layers = nn.ModuleList(
-            [
-                TransformerBlock(
-                    embed_size,
-                    heads,
-                    dropout=dropout,
-                    forward_expansion=forward_expansion,
-                )
-                for _ in range(num_layers)
-            ]
-        )
+            [TransformerBlock(embed_size, heads, dropout=dropout, forward_expansion=forward_expansion,) 
+             for _ in range(num_layers)])
 
         self.dropout = nn.Dropout(dropout)
 
     def forward(self, x, mask):
         N, seq_length = x.shape
         positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
-        out = self.dropout(
-            (self.word_embedding(x) + self.position_embedding(positions))
-        )
+        out = self.dropout((self.word_embedding(x) + self.position_embedding(positions)))
 
         # In the Encoder the query, key, value are all the same, it's in the
         # decoder this will change. This might look a bit odd in this case.
@@ -168,28 +148,15 @@ def forward(self, x, value, key, src_mask, trg_mask):
 
 
 class Decoder(nn.Module):
-    def __init__(
-        self,
-        trg_vocab_size,
-        embed_size,
-        num_layers,
-        heads,
-        forward_expansion,
-        dropout,
-        device,
-        max_length,
-    ):
+    def __init__(self, trg_vocab_size, embed_size, num_layers, heads, forward_expansion,dropout, device, max_length,):
         super(Decoder, self).__init__()
         self.device = device
         self.word_embedding = nn.Embedding(trg_vocab_size, embed_size)
         self.position_embedding = nn.Embedding(max_length, embed_size)
 
         self.layers = nn.ModuleList(
-            [
-                DecoderBlock(embed_size, heads, forward_expansion, dropout, device)
-                for _ in range(num_layers)
-            ]
-        )
+            [DecoderBlock(embed_size, heads, forward_expansion, dropout, device)
+                for _ in range(num_layers)])
         self.fc_out = nn.Linear(embed_size, trg_vocab_size)
         self.dropout = nn.Dropout(dropout)
 
@@ -223,28 +190,8 @@ def __init__(
     ):
 
         super(Transformer, self).__init__()
-
-        self.encoder = Encoder(
-            src_vocab_size,
-            embed_size,
-            num_layers,
-            heads,
-            device,
-            forward_expansion,
-            dropout,
-            max_length,
-        )
-
-        self.decoder = Decoder(
-            trg_vocab_size,
-            embed_size,
-            num_layers,
-            heads,
-            forward_expansion,
-            dropout,
-            device,
-            max_length,
-        )
+        self.encoder = Encoder(src_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length,)
+        self.decoder = Decoder(trg_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length,)
 
         self.src_pad_idx = src_pad_idx
         self.trg_pad_idx = trg_pad_idx
@@ -257,10 +204,7 @@ def make_src_mask(self, src):
 
     def make_trg_mask(self, trg):
         N, trg_len = trg.shape
-        trg_mask = torch.tril(torch.ones((trg_len, trg_len))).expand(
-            N, 1, trg_len, trg_len
-        )
-
+        trg_mask = torch.tril(torch.ones((trg_len, trg_len))).expand(N, 1, trg_len, trg_len)
         return trg_mask.to(self.device)
 
     def forward(self, src, trg):
@@ -275,17 +219,15 @@ def forward(self, src, trg):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     print(device)
 
-    x = torch.tensor([[1, 5, 6, 4, 3, 9, 5, 2, 0], [1, 8, 7, 3, 4, 5, 6, 7, 2]]).to(
-        device
-    )
+    x = torch.tensor([[1, 5, 6, 4, 3, 9, 5, 2, 0],[1, 8, 7, 3, 4, 5, 6, 7, 2]]).to(device)
     trg = torch.tensor([[1, 7, 4, 3, 5, 9, 2, 0], [1, 5, 6, 2, 4, 7, 6, 2]]).to(device)
 
     src_pad_idx = 0
     trg_pad_idx = 0
     src_vocab_size = 10
     trg_vocab_size = 10
-    model = Transformer(src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, device=device).to(
-        device
-    )
+    
+    model = Transformer(src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, device=device).to(device)
+    
     out = model(x, trg[:, :-1])
     print(out.shape)