Program Plan: Include the HTML tag using <HTML>. Include the header tag using <head>. Include the title on the webpage using <title> tag. Include a hyperlink to the named fragment designated by “top”. Close the header tag. Include an internal CSS file using the style tag. Include alignment For Body Tag. Include alignment for CSS Container. Alignment to have the image in the right side of the webpage. Alignment for aside tag to place some content aside from the content it is placed in. Include the body using the <body> tag. Include a heading using the <h1> tag. Include a paragraph using the <p> tag. Close the body using </body> tag. Close the file using </html> tag.

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Chapter 7, Problem 9HOE

Program Plan Intro

Program Plan:

Include the HTML tag using <HTML>.

Include the header tag using <head>.

Include the title on the webpage using <title> tag.

Include a hyperlink to the named fragment designated by “top”.

Close the header tag.

Include an internal CSS file using the style tag.

Include alignment For Body Tag.

Include alignment for CSS Container.

Alignment to have the image in the right side of the webpage.

Alignment for aside tag to place some content aside from the content it is placed in.

Include the body using the <body> tag.

Include a heading using the <h1> tag.

Include a paragraph using the <p> tag.

Close the body using </body> tag.

Close the file using </html> tag.

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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 -->…

Please provide me with the output image of both of them . below are the diagrams code I have two diagram : first diagram code 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)…