Calculate Root Mean Square Error (RMSE) Task: Compute Root Mean Square Error (RMSE) In this task, you are required to implement a functionrmse(y_true, y_pred)that calculates the Root Mean Square Error (RMSE) between the actual values and the predicted values. RMSE is a commonly used metric for evaluating the accuracy of regression models, providing insight into the standard deviation of residuals. Your Task: Implement the functionrmse(y_true, y_pred)to: Calculate the RMSE between the arraysy_trueandy_pred. Return the RMSE value rounded to three decimal places. Ensure the function handles edge cases such as: Mismatched array shapes. Empty arrays. Invalid input types. The RMSE is defined as: RMSE=1ni=1n(ytrue,iypred,i)2RMSE=n1i=1n(ytrue,iypred,i)2 Where: nnis the number of observations. ytrue,iytrue,iandypred,iypred,iare the actual and predicted values for theii-th observation. Example: Input: y_true = np.array([3, -0.5, 2, 7]) y_pred = np.array([2.5, 0.0, 2, 8]) print(rmse(y_true, y_pred)) Output: 0.612 import numpy as np def rmse(y_true, y_pred): # Write your code here return round(rmse_res,3)