Implement Linear Regression with Backpropagation

Implement a linear regression model using backpropagation to update model parameters. The input consists of a 2D array X and a 1D array y representing the dataset, where each row of X is a sample and each column is a feature, with y being the corresponding labels. The output should be the predicted results and the trained model parameters. Input A 2D array X with dimensions m x n, where m is the number of samples and n is the number of features. A 1D array y of size m, representing the label for each sample. A float learning_rate representing the learning rate. An integer epochs representing the number of training iterations. Output A 1D array predictions, representing the results of the model prediction. A 1D array weights and a float bias, representing the trained model parameters. Example Input: X = [[1, 2], [3, 4], [5, 6]] y = [3, 7, 11] learning_rate = 0.01 epochs = 1000 Output: predictions = [3.0, 7.0, 11.0] weights = [1.0, 1.0] bias = 0.0 Data Limits m and n are both up to 1000. Example Input 3 2 1 2 3 4 5 6 3 7 11 0.01 1000