I’m trying to implement SPSA(Simultaneous perturbation stochastic approximation) as a subclass of the optimizer class in TensorFlow. When I call loss in compute_gradients() method I get an error “TypeError: ‘Tensor’ object is not callable”. How can this be fixed?

    def compute_gradients(self, loss, var_list, tape=None):
        if not callable(loss) and tape is None:
            raise ValueError(
                "`tape` is required when a `Tensor` loss is passed. "
                f"Received: loss={loss}, tape={tape}."
            )
        if tape is None:
            tape = tf.GradientTape()
        if callable(loss):
            with tape:
                if not callable(var_list):
                    tape.watch(var_list)
                loss = loss()
                if callable(var_list):
                    var_list = var_list()
        grads = []
        gamma = tf.cast(self.gamma, dtype=tf.float32)
        for var in var_list:
            if var is not None:
                step = tf.cast(self.iterations + 1, dtype=tf.float32)
                c_t = self.c / step ** gamma
                delta = np.random.uniform(0, 1, size=var.shape)
                delta[delta < 0.5] = -1
                delta[delta > 0.5] = 1
                y_plus = loss(var + tf.multiply(c_t, delta))
                y_minus = loss(var - tf.multiply(c_t, delta))
                delta_norm = tf.cast(tf.norm(delta), dtype=tf.float32)
                grad = (y_plus - y_minus)
                grad /= (2*tf.norm(c_t)*delta_norm)
            else:
                grad = None
            grads.append(grad)
        return list(zip(grads, var_list))