def squared_error_gradient(x_i, y_i, beta):
 return [(((-2) * x_ij) * error(x_i, y_i, beta)) for x_ij in x_i]

def squared_error_gradient ( x_i , y_i , beta ) : return [ ( ( ( - 2 ) * x_ij ) * error ( x_i , y_i , beta ) ) for x_ij in x_i ]
the gradient corresponding to the ith squared error term .