 tf.keras.callbacks.ModelCheckpoint: initial_value_threshold not working, definition with a mistake
@agoeroeg Thanks for reporting the issue.

I tried to replicate the issue in TF v2.3 and and in the latest TF v2.11 using a [simple MNIST convnet](https://keras.io/examples/vision/mnist_convnet/) .

I have observed the reported behaviour in TF 2.3 where as in TF 2.11 I was able to get the `initial_value_threshold` work with the given value. 

Please find the gist of TF 2.3 [here](https://colab.research.google.com/gist/pjpratik/1fb72958bcf1ff03ff61634f9e3cfe03/59577_2-3.ipynb) and gist of TF 2.11 [here](https://colab.research.google.com/gist/pjpratik/6feee0f23ba7e67e1ca3b8a8868e73b5/59577_2-11.ipynb) and let us know if it helps. Thanks! This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.
 Closing as stale. Please reopen if you'd like to work on this further.
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Documentation Bug

### Have you reproduced the bug with TF nightly?

No

### Source

source

### Tensorflow Version

'2.3.0'

### Custom Code

Yes

### OS Platform and Distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current Behaviour?

```shell
The manual defines initial_value_threshold as 
"Floating point initial "best" value of the metric to be monitored. Only applies if save_best_value=True. Only overwrites the model weights already saved if the performance of current model is better than this value. "

save_best_value is not defined!

If  save_best_only was meant, then it is not working like the manual says.
I have no other idea, how should I get initial_value_threshold work, it is still starting with -inf.
```


### Standalone code to reproduce the issue

```shell
es = EarlyStopping(monitor='val_loss', verbose=1, patience=20)
tmpmodelfile = 'models/' + model_config_name + '/temp1_best_model.h5'

evaluation = model.evaluate(vd[feature_cols], vd[target], 
                            batch_size=batch_size, verbose=2,
                            return_dict=True)
#3231/3231 - 2s - loss: 0.0500 - mean_squared_error: 0.0500
mc = ModelCheckpoint(tmpmodelfile, monitor='val_loss', 
                     verbose=1, save_best_only=True, 
                     initial_value_threshold=evaluation['loss'],
                     save_best_value=True)
history = model.fit(td[feature_cols], td[target], 
                    validation_data=(vd[feature_cols], vd[target]), 
                    batch_size=batch_size, epochs=epochs, 
                    verbose=2, callbacks=[es, mc])
Epoch 1/400
Epoch 00001: val_loss improved from inf to 0.04999, saving model to models
```


### Relevant log output

_No response_</details>