the below  is an example of diabetes dataset import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_diabetes from sklearn import linear_model d = load_diabetes() d_X = d.data[:, np.newaxis, 2] dx_train = d_X[:-20] dy_train = d.target[:-20] dx_test = d_X[-20:] dy_test = d.target[-20:] lr = linear_model.LinearRegression() lr.fit(dx_train, dy_train) mse = np.mean((lr.predict(dx_test) - dy_test) **2) lr_score = lr.score(dx_test, dy_test) print(lr.coef_) print(mse) print(lr_score) plt.scatter(dx_test, dy_test) plt.plot(dx_test, lr.predict(dx_test), c='r') plt.show()

Please debug the following trenchTranveral javascript function. The function should return true if the node's value is less than -5 and false if equal to or greater than -5.     function findNeighbors(node, matrix) { const neighbors = []; const [row, col] = node; const currNum = matrix[row][col]; // Check top if (matrix[row - 1] && Math.abs(matrix[row - 1][col] < - 5)) { neighbors.push([row - 1, col]); } // Check bottom if (matrix[row + 1] && Math.abs(matrix[row + 1][col] < - 5)) { neighbors.push([row + 1, col]); } // Check left if (matrix[row][col - 1] && Math.abs(matrix[row][col - 1] < - 5)) { neighbors.push([row, col - 1]); } // Check right if (matrix[row][col + 1] && Math.abs(matrix[row][col + 1] < - 5)) { neighbors.push([row, col + 1]); } // Return neighbors return neighbors; // Only consider N, S, E, W nodes } function trenchTraversal(node, matrix, visited) { const [row, col] = node; let stack = [node] visited.add(node.toString())…