Mustufain

    def backward(self, dZ):
        """
        Backward proporgation for convolution.

        Parameters:
        dZ -- gradient of the cost with respect to the output of the conv layer (Z), numpy array of shape (m, n_H, n_W, n_C)

        Returns:
        dA_prev -- gradient of the cost with respect to the input of the conv
                   layer (A_prev), numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)