Ds- S₁ S2 S3 D₁ T₁ Encoder Output (shifted right) Output Embedding Input Embedding Attention Muti-Head Add & Norm Feed Forward Add & Norm Muti-Head Add & Attention Norm Encoder #N Muti-Head Add & Attention Norm Feed Forward Add & Norm Decoder #N Linear T₁ S₁ T₁ S₁ S₂ S3 Linear Кт VT Qs Vs Ks Cross Adaptive Layer Sigmoid Muti-Head Cross Attention Muti-Head Attention Add & Norm Add & Norm S₂ ypred T₁ S₁ S₁ LMMD LMSE Feed Forward Feed Forward Ldistillation S3 Ylabel Add & Norm Add & Norm ΤΙ S₁ Cross Adaptive Layer |Ltotal = arg min (WaLdistillation+ WMLMMD + W,Lregression) Fig. 6. Architecture of the proposed MSCATN.

Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN).

graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation --> Student_B_Encoder end Teacher_Prediction_B -- Logits --> Logits_Distillation_B Teacher_Features_B -- Features --> Feature_Alignment_B Feature_Alignment_B -- Feature Alignment Loss (L_feature_B) --> Knowledge_Distillation_B Logits_Distillation_B -- Logits Distillation Loss (L_logits_B) --> Knowledge_Distillation_B Partial_Labels_B -- Available Labels --> Prediction_Loss_B Prediction_Loss_B -- Prediction Loss (L_pred_B) --> Knowledge_Distillation_B style Knowledge_Distillation_B fill:#aed,stroke:#333,stroke-width:2px style Total_Loss_B fill:#fff,stroke:#333,stroke-width:2px

graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A) --> Total_Loss_A Total_Loss_A -- Backpropagation --> Student_A_Encoder end Teacher_Prediction -- Logits --> Logits_Distillation_A Teacher_Features -- Features --> Feature_Alignment_A Feature_Alignment_A -- Feature Alignment Loss (L_feature_A) --> Knowledge_Distillation_A Logits_Distillation_A -- Logits Distillation Loss (L_logits_A) --> Knowledge_Distillation_A Ground_Truth_A -- Labels --> Prediction_Loss_A Prediction_Loss_A -- Prediction Loss (L_pred_A) --> Knowledge_Distillation_A style Knowledge_Distillation_A fill:#ccf,stroke:#333,stroke-width:2px style Total_Loss_A fill:#fff,stroke:#333,stroke-width:2px

i have also attached the diagram code for both for you reference the two diagram must be very explicit 

please there were an answwer which did not satisfy my need