Fix bart shape comment (#8423)

src/transformers/modeling_bart.py

@@ -585,7 +585,7 @@ class BartDecoder(nn.Module):
 
         x = F.dropout(x, p=self.dropout, training=self.training)
 
-        # Convert to Bart output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
+        # Convert to Bart output format: (BS, seq_len, model_dim) ->  (seq_len, BS, model_dim)
         x = x.transpose(0, 1)
         encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
 

src/transformers/modeling_tf_bart.py

@@ -570,7 +570,7 @@ class TFBartDecoder(Layer):
             x = self.layernorm_embedding(x + positions)
         x = tf.nn.dropout(x, rate=self.dropout if training else 0)
 
-        # Convert to Bart output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
+        # Convert to Bart output format: (BS, seq_len, model_dim) ->  (seq_len, BS, model_dim)
         x = tf.transpose(x, perm=(1, 0, 2))
         assert len(shape_list(encoder_hidden_states)) == 3, "encoder_hidden_states must be a 3D tensor"
         encoder_hidden_states = tf.transpose(encoder_hidden_states, perm=(1, 0, 2))