Solution

class Solution:
    def compute_gradient(self, a:float, b:float, c:float, x:float):
        """
        Function to compute gradient of f(x) w.r.t to each parameter
        Args:
        a,b,c : coeffients to determine quadratic f(x)
        x : x axis value at which gradient to be calculated
        Return:
        d_x : gradient w.r.t x
        """
        d_x = 2 * a * x + b
        return d_x

    def compute_minima(self, a:float, b:float, c:float, learning_rate, number_of_iteration):
        """
        Function to  minimise f(x) = a*x^2 + b*x + c
        Args:
        a,b,c : coeffients to determine quadratic f(x)
        learning_rate : learning rate in gradient descent
        number_of_iteration: number of time gradient update to be done
        Return:
        x value where f(x) is minimum
        """
        if a <= 0:
            return "Minima can not be found"
        x = 0 # initial value , you can explore other ways to initilaize this
        for i in range(number_of_iteration):
            d_x = self.compute_gradient(a,b,c,x)
            x = x - learning_rate * d_x
        return x