keras.model.predict Generates Extra Batch and Inconsistent Number of Calls with Generator Dataset
Hi @arthurflor23 ,

I have replicated the reported behaviour with tf-nightly and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/72026e6487102834b802491f531e7b53/61169.ipynb) here. It seems valid bug for me and needs to dig more.

Thanks ! Hi,
Any news on this?
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.14.0.dev20230703

### Custom code

Yes

### OS platform and distribution

Linux

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

When providing a Python generator to the `keras.Model.predict` function with a specified number of steps (`N`), the generator is called `N+1` times instead of `N` times. This behavior causes difficulties in resuming predictions and leads to an unused batch in the prediction output.

I found the same issue on the TensorFlow GitHub repository ([link to issue](https://github.com/tensorflow/tensorflow/issues/45459)). However, the issue was closed without a resolution, and the problem persists in my environment today. I believe this issue is worth revisiting and resolving, as it affects the predict function when using a generator dataset.

### Standalone code to reproduce the issue

I managed to replicate the problem in the same gist reported before ([gist for nightly](https://colab.research.google.com/gist/Saduf2019/552f6674aa5f547a6f2898c1a4edf9c2/untitled.ipynb))

### Relevant log output

_No response_