Custom Optimizers not working [BUG]

**To Reproduce run following**
```
class MyMomentumOptimizer(tf.keras.optimizers.Optimizer):
def __init__(self, learning_rate=0.001, momentum=0.9, name="MyMomentumOptimizer", **kwargs):
"""Call super().__init__() and use _set_hyper() to store hyperparameters"""
super().__init__('MyMomentumOptimizer', **kwargs)
self._set_hyper("learning_rate", kwargs.get("lr", learning_rate)) # handle lr=learning_rate
self._set_hyper("decay", self._initial_decay) #
self._set_hyper("momentum", momentum)

def _create_slots(self, var_list):
"""For each model variable, create the optimizer variable associated with it.
TensorFlow calls these optimizer variables "slots".
For momentum optimization, we need one momentum slot per model variable.
"""
for var in var_list:
self.add_slot(var, "momentum")

@tf.function
def _resource_apply_dense(self, grad, var):
"""Update the slots and perform one optimization step for one model variable
"""
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype) # handle learning rate decay
momentum_var = self.get_slot(var, "momentum")
momentum_hyper = self._get_hyper("momentum", var_dtype)
momentum_var.assign(momentum_var * momentum_hyper - (1. - momentum_hyper)* grad)
var.assign_add(momentum_var * lr_t)

def _resource_apply_sparse(self, grad, var):
raise NotImplementedError

def get_config(self):
base_config = super().get_config()
return {
**base_config,
"learning_rate": self._serialize_hyperparameter("learning_rate"),
"decay": self._serialize_hyperparameter("decay"),
"momentum": self._serialize_hyperparameter("momentum"),
}
```

Following Exception Occured

`AttributeError: 'MyMomentumOptimizer' object has no attribute '_set_hyper'
`
**Versions (please complete the following information):**
- OS: [Debian 4.19.269-1]
- Python: [3.7]
- TensorFlow: [2.11]

**Additional context**
After taking a look at base class I couldn't find `_set_hyper` in base class