How to create `tf.keras.layers.Input` without batch_size dimension?
@baoachun 
This issue seems to be keras issue.Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)
To know more see;
[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999).

Thank you! 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

Support

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

No

### Source

source

### Tensorflow Version

2.9.1

### Custom Code

No

### OS Platform and Distribution

centos7

### Mobile device

_No response_

### Python version

3.8.14

### Bazel version

5.0

### GCC/Compiler version

10.2

### CUDA/cuDNN version

11.4

### GPU model and memory

A30

### Current Behaviour?

One input of my model has nothing to do with batch_size, for example, its shape is [1,2,3], how to avoid automatically adding 1 dimension when creating  `tf.keras.layers.Input` ? If I manually slice it, the slice operator will be introduced, resulting in a decrease in inference performance.


### Standalone code to reproduce the issue

```shell
import tensorflow as tf

x = tf.keras.layers.Input(shape=(32,64))
# x.shape: (None, 32, 64)
x = x[0, :, :]
# x.shape: (32, 64)

```
