Tensorflow 2.11 Optimizers. Does anyone have any custom optimizer for the new version?

[thewizardnet]

I know that we can use tf.keras.optimizers.legacy.Optimizer for making the older custom optimizers to work,but I'm wonder how I can update my code.This the original code that I want to make it function for tf 2.11

`class Gravity(tf.keras.optimizers.Optimizer): def init(self, learning_rate=0.1, alpha=0.01, beta=0.9, name="Gravity", kwargs): super(Gravity, self).init(name, kwargs) self._set_hyper('learning_rate', kwargs.get('lr', learning_rate)) self._set_hyper('decay', self._initial_decay) self._set_hyper('alpha', alpha) self._set_hyper('beta', beta) self.epsilon = 1e-7

def _create_slots(self, var_list):
    alpha = self._get_hyper("alpha")
    stddev = alpha / self.learning_rate
    initializer = tf.keras.initializers.RandomNormal(mean=0.0,
                                                     stddev=stddev,
                                                     seed=None)
    for var in var_list:
        self.add_slot(var, "velocity", initializer=initializer)

@tf.function
def _resource_apply_dense(self, grad, var):
    # Get Data
    var_dtype = var.dtype.base_dtype
    lr_t = self._decayed_lr(var_dtype)
    beta = self._get_hyper("beta", var_dtype)
    t = tf.cast(self.iterations, float)
    beta_hat = (beta * t + 1) / (t + 2)
    velocity = self.get_slot(var, "velocity")

    # Calculations
    max_step_grad = 1 / tf.math.reduce_max(tf.math.abs(grad))
    gradient_term = grad / (1 + (grad / max_step_grad)**2)

    # update variables
    updated_velocity = velocity.assign(beta_hat * velocity +
                                       (1 - beta_hat) * gradient_term)
    updated_var = var.assign(var - lr_t * updated_velocity)

    # updates = [updated_var, updated_velocity]
    # return tf.group(*updates)
def _resource_apply_sparse(self, grad, var):
    raise NotImplementedError

def get_config(self):
    config = super(Gravity, self).get_config()
    config.update({
        'learning_rate':
        self._serialize_hyperparameter('learning_rate'),
        'decay':
        self._serialize_hyperparameter('decay'),
        'alpha':
        self._serialize_hyperparameter('alpha'),
        'beta':
        self._serialize_hyperparameter('beta'),
        'epsilon':
        self.epsilon,
    })
    return config`