+ Code + Text Forward pass Q The linear regression model is shown in the following figure: b II + : : In The forward pass of a given input = 11,...,xnis y = fw6(2) =w+b = 2 wit... + x + b Implement the forward pass below that takes inputs x, weights, and biases as its arguments and returns the prediction . Note that your function should be able to work for multiple inputs. For example x can be a matrix where each row is a particular data point: (1) X= 2 : (m) (m) In this case the output should be B = X.w+b. (m) >- Implement the forward_pass function below that takes inputs x, weights, and biases as its arguments and returns the prediction . Note that your function should be able to work for multiple inputs. For example x can be a matrix where each row is a particular data point: (1) In X= (m) : (m) Un In this case the output should be y(1) = X.w+b. (m) [ ] def forward_pass(x, weights, biases): " Computes the predictions for a given set of data points Arguments: x: a numpy array of shape (m, n) weights: the weight vector, a numpy array of shape (n,1) biases: the bias, a numpy array of shape (1,1) Returns: y_hat: the predictions, a numpy array of shape (m,1) ################### Your code goes here ################ ######## ############ [] project_2_tests.test_forward_pass_linear regression (forward_pass) + Code + Text Forward pass Q The linear regression model is shown in the following figure: b II + : : In The forward pass of a given input = 11,...,xnis y = fw6(2) =w+b = 2 wit... + x + b Implement the forward pass below that takes inputs x, weights, and biases as its arguments and returns the prediction . Note that your function should be able to work for multiple inputs. For example x can be a matrix where each row is a particular data point: (1) X= 2 : (m) (m) In this case the output should be B = X.w+b. (m) >- Implement the forward_pass function below that takes inputs x, weights, and biases as its arguments and returns the prediction . Note that your function should be able to work for multiple inputs. For example x can be a matrix where each row is a particular data point: (1) In X= (m) : (m) Un In this case the output should be y(1) = X.w+b. (m) [ ] def forward_pass(x, weights, biases): " Computes the predictions for a given set of data points Arguments: x: a numpy array of shape (m, n) weights: the weight vector, a numpy array of shape (n,1) biases: the bias, a numpy array of shape (1,1) Returns: y_hat: the predictions, a numpy array of shape (m,1) ################### Your code goes here ################ ######## ############ [] project_2_tests.test_forward_pass_linear regression (forward_pass)