solve using python

Task 6. Integrate into one model 3 points Integrate the above parts into one model, and apply the model on the train data (Xtrain,Ytrain), and then evaluate on test data (Xtest,Ytest). Hint: - You need to call init_zeros and GD in order. You should pass the initialized parameters and all the other necessary arguments to GD. - You also need to compute the accuray on train and test data respectively. Accuray is defined by the fraction of correct predictions over total number examples. \#\#\#\# DO NOT CHANGE THE CODE BELOW \#\#\#\# \# Evaluate Task 6 res = model(X_train, Y_train, X_test, Y_test, num_iters=1500, alpha=0.002, verbose=True) \# Plot learning curve costs =np.squeeze(res[ 'costs ' ]) plt.plot(costs) plt.ylabel('cost') plt.xlabel ('iterations (per hundreds)') plt.title('Learning rate =0.002 ) plt. show () Expected output Cost after iter 0: |0.6931471805599454 Cost after iter 100: |0.5946773825987639 Cost after iter 200: |0.5256364501984687 Cost after iter 300: |0.4747208768166399 Cost after iter 400: |0.435436416758632 Cost after iter 500: |0.40399872095331557 Cost after iter 600: |0.37811027839268685 Cost after iter 700: |0.35630887692114865 Cost after iter 800: |0.3376209341419335 Cost after iter 900: |0.32137148224069756 Cost after iter 1000: 0.30707586651947666 Cost after iter 1100: |0.29437547177794215 Cost after iter 1200: 0.28299807348845724 Cost after iter 1300 : 0.27273248705908887 Cost after iter 1400: |0.26341182071904296 train accuracy: | 94.05594405594405% test accuracy: | 88.0%