Error converting `None` to a tensor in tensorflow 2.12.0
The error message suggests that there is an issue with converting **None** values to a tensor. Specifically, the error occurs when trying to convert elements of **[1, 1, 1, None, 1]** to a **tensor**. The error message also points to a layer called **conv1_bn** of type **BatchNormalization** that encountered the error. @dstansby,
 In order to expedite the trouble-shooting process, could you please provide a complete code snippet you are using which helps us to analyse the issue in an effective way. Thank you!
 The error message suggests that there is an issue with converting elements of [1, 1, 1, None, 1] to a tensor. The presence of None in the list might be causing the problem.

Without a complete code example, it's difficult to provide a specific solution. However, based on the information provided, you can try the following general suggestions:

Ensure that the dimensions of your input data are correctly specified. It seems that there is a None value in the dimensions of the input tensor, which might be causing the issue. If you're using a custom input shape, double check that it's specified correctly.

If the None value is expected (for instance, to represent a dynamic batch size), you can try specifying the input shape of your model explicitly to include the None value. For example:

input_shape = (None, height, width, channels)
input_layer = keras.Input(shape=input_shape)


If you're using a custom layer or modifying the behavior of an existing layer, ensure that it properly handles None values in the input shape. You may need to add conditional logic to handle cases when the shape contains None.

Check your data preprocessing pipeline to ensure it generates data with the correct shape and type. Ensure that the pipeline doesn't introduce any unexpected None values in the data.

If these suggestions don't help, please provide a minimal reproducible example that showcases the issue. This will make it easier to diagnose the problem and offer a specific solution. This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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 ### Issue Type

Bug

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

No

### Source

source

### Tensorflow Version

2.12.0

### 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?

As of tensorflow 2.12.0, I'm seeing a new error related to converting `None` values to a tensor. I haven't managed to produce a reproducible example (sorry!), but thought I'd post here anyway to see if anyone has any insight? Traceback is:

```python
  iterator = <tensorflow.python.data.ops.iterator_ops.OwnedIterator object at 0x7f536c61c550>
  
      def tf__predict_function(iterator):
          """Runs an evaluation execution with a single step."""
          with ag__.FunctionScope('predict_function', 'fscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True)) as fscope:
              do_return = False
              retval_ = ag__.UndefinedReturnValue()
              try:
                  do_return = True
  >               retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
  E               TypeError: in user code:
  E               
  E                   File "/home/runner/work/cellfinder-core/cellfinder-core/.tox/py310/lib/python3.10/site-packages/keras/engine/training.py", line 2169, in predict_function  *
  E                       return step_function(self, iterator)
  E                   File "/home/runner/work/cellfinder-core/cellfinder-core/.tox/py310/lib/python3.10/site-packages/keras/engine/training.py", line 2155, in step_function  **
  E                       outputs = model.distribute_strategy.run(run_step, args=(data,))
  E                   File "/home/runner/work/cellfinder-core/cellfinder-core/.tox/py310/lib/python3.10/site-packages/keras/engine/training.py", line 2143, in run_step  **
  E                       outputs = model.predict_step(data)
  E                   File "/home/runner/work/cellfinder-core/cellfinder-core/.tox/py310/lib/python3.10/site-packages/keras/engine/training.py", line 2111, in predict_step
  E                       return self(x, training=False)
  E                   File "/home/runner/work/cellfinder-core/cellfinder-core/.tox/py310/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
  E                       raise e.with_traceback(filtered_tb) from None
  E               
  E                   TypeError: Exception encountered when calling layer 'conv1_bn' (type BatchNormalization).
  E                   
  E                   Failed to convert elements of [1, 1, 1, None, 1] to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.
  E                   
  E                   Call arguments received by layer 'conv1_bn' (type BatchNormalization):
  E                     • inputs=tf.Tensor(shape=(None, None, None, None, 64), dtype=float32)
  E                     • training=False
  E                     • mask=None
  
  /tmp/__autograph_generated_filerbzi9zef.py:15: TypeError
```

### Standalone code to reproduce the issue

```shell
~
```


### Relevant log output

_No response_