Clippy_adagrad raised an error: NotImplementedError: _resource_apply_dense must be implemented in subclasses.

env:
gcc version 9.3.0
Description: Ubuntu 20.04.4 LTS
Python 3.10.16
tensorflow 2.9.0
tensorflow-estimator 2.9.0
tensorflow-io 0.37.1
tensorflow-io-gcs-filesystem 0.37.1
tensorflow-recommenders 0.7.3
keras 2.9.0

traceback:
File "/data1/env/miniconda3/envs/tf2.18/lib/python3.10/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize
return self.apply_gradients(grads_and_vars, name=name)
File "/data1/env/miniconda3/envs/tf2.18/lib/python3.10/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients
return tf.internal.distribute.interim.maybe_merge_call(
File "/data1/env/miniconda3/envs/tf2.18/lib/python3.10/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply
update_op = distribution.extended.update(
File "/data1/env/miniconda3/envs/tf2.18/lib/python3.10/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 706, in apply_grad_to_update_var **
update_op = self._resource_apply_dense(grad, var, **apply_kwargs)
File "/data1/env/miniconda3/envs/tf2.18/lib/python3.10/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 1298, in _resource_apply_dense
raise NotImplementedError("_resource_apply_dense must be implemented in "

NotImplementedError: _resource_apply_dense must be implemented in subclasses.

Start by reproducing the traceback with the listed Python, TensorFlow, Keras, and tensorflow-recommenders versions. Inspect the optimizer call path ending at keras/optimizers/optimizer_v2/optimizer_v2.py:1298; done requires determining the compatibility or implementation issue responsible for the missing _resource_apply_dense method.