tf.keras.layers.Dense throws error when backprop
Hi @cheyennee ,

I have replicated the reported behaviour with TF2.14 and tf-nightly as well. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/5df36464b3a5a49174b297d517224e81/62432.ipynb) for reference.

Will dig more into for the root cause and come back. Thanks! Hi @SuryanarayanaY ,
I believe the bug stems from the absence of units validation. As per the [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense), units should be a positive integer. However, in the provided code, when units is set to 0, no error is raised during the execution of res = layer(inputs). @cheyennee ,

Making `units>0` indeed resolves this issue. I can see validation for `units < 0` not `units <= 0`. This needs to check internally. 
### Issue type

Bug

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

No

### Source

source

### TensorFlow version

tf 2.14.0

### Custom code

Yes

### OS platform and distribution

windows colab

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

tf.keras.layers.Dense crashes in backprop.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
units = 0
activation = "sigmoid"
use_bias = True
kernel_initializer = "ones"
bias_initializer = "zeros"
kernel_regularizer = None
bias_regularizer = None
activity_regularizer = None
kernel_constraint = None
bias_constraint = None
__input___0_tensor = tf.random.uniform([50, 1], minval=1.0, maxval=3.0, dtype=tf.float64)
__input___0 = tf.identity(__input___0_tensor)
Dense_class = tf.keras.layers.Dense(units, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint)

layer = Dense_class
inputs = __input___0

with tf.GradientTape() as g:
    g.watch(inputs)
    res = layer(inputs)
print(res.shape)
grad = g.jacobian(res, inputs)  # Error
print(grad)
```


### Relevant log output

```shell
(50, 0)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-b51b65c26b37> in <cell line: 23>()
     21     res = layer(inputs)
     22 print(res.shape)
---> 23 grad = g.jacobian(res, inputs)  # Error
     24 print(grad)

1 frames
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py in set_shape(self, shape)
    534   def set_shape(self, shape) -> None:
    535     if not self.shape.is_compatible_with(shape):
--> 536       raise ValueError(f"Tensor's shape {self.shape} is not compatible "
    537                        f"with supplied shape {shape}.")
    538 

ValueError: Tensor's shape (50, 0, 50, 0) is not compatible with supplied shape (50, 0, 50, 1).
```
