Problem Solving with C++ (10th Edition)
Problem Solving with C++ (10th Edition)
10th Edition
ISBN: 9780134521176
Author: SAVITCH
Publisher: PEARSON
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Chapter 9.2, Problem 12STE
Program Plan Intro

Pointer in C++:

A pointer is a variable whose value will be another variable’s address. Generally, a pointer variable is declared as follows:

type *var-name;

Here, type is the pointer’s base type and var-name is the pointer variable name. The asterisk is used to designate a variable as a pointer.

Given Program:

//Include libraries

#include <iostream>

//Use namespace

using namespace std;

//Define main method

int main()

{

// Declare array

int a[10];

// Declare variable

int *p = a;

// Declare variable

int i;

//Loop executes until the value of i exceeds 10

for (i = 0; i < 10; i++)

//Assign value i to a[i]

a[i] = i;

//Loop executes until the value of i exceeds 10

for (i = 0; i < 10; i++)

//Display value

cout << p[i] << " ";

cout << endl;

//Pause console window

system("pause");

//Return 0 value

return 0;

}

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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)…
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