What's wrong with my code $?$ I Got the following feedback : $"$ Test Failed: The SVM classifier evaluation metrics are not found because evaluate$\_$svm$\_$classifier function is not implemented or not working as intended."

In $[4]$: import numpy as np

from sklearn.neighbors import KNeighborsClassifier

from sklearn.svm import SVC

from sklearn.metrics import accuracy$\_$score, precision$\_$score, recall$\_$score, confusion$\_$matrix

def evaluate$\_$svm$\_$classifier$($X$\_$train, y$\_$train, X$\_$test, y$\_$test$)$:

evaluate$\_$Svm$\_$classifier $=$ SVC$($kernel $=$ 'linear'$)$

evaluate$\_$svm$\_$classifier.fit$($x$\_$train, y$\_$train$)$

y$\_$pred $=$ evaluate$\_$svm$\_$classifier.predict$($x$\_$test$)$

precision $=$ precision$\_$score $($y$\_$test, y$\_$pred$)$

recall $=$ recall$\_$score$($y$\_$test, y$\_$pred$)$

slope $=-$evaluate$\_$svm$\_$classifier.coef$\_[0][0]/$ svm$\_$clf$.$coef$\_[0][1]$

intercept $=-$evaluate$\_$svm$\_$classifier.intercept$\_[0]/$ svm$\_$cl$$f$.$coef$\_[0][1]$

false$\_$positives, false$\_$negatives $=$ confusion$\_$m$$trix$($y$\_$tes$$t$,$ y$\_$pr$).$ravel$()$

Freturn slope, intercept, accuracy, precision, recall, false$\_$positives, false$\_$negatives

$"""$

Trains an SVM classifier with a linear kernel on the training set and evaluates its performance on the test set.

Parameters:

X$\_$train: Training data features.

y$\_$train: Training data labels.

X$\_$test: Test data features.

y$\_$test: Test data labels.

Returns:

Slope and intercept of the decision boundary.

Accuracy, precision, recall on the test set.

Number of false positives and false negatives.

$"""$