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Tensorflow: Best practices to write functions for both, modes, eager and graph

Best practices to write functions for both, modes, eager and graph in Tensorflow

I regularly run into the problem that I have a Python function that I want to use in both, eager and graph execution mode. I therefore have to adjust the code so that it can handle both situations. Here are two examples:
```python
import tensorflow as tf
def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
""" convert signal to noise ratio (SNR) from linear to dB """
if tf.is_tensor(x):
return tf.math.log(x) * (10. / tf.math.log(10.))
else:
return math.log10(x) * 10.
def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
return int(x) if tf.executing_eagerly() else x
```
~~Are there best practices for such functions? Or maybe helpful predefined functions from tensorflow which make my life easier?~~

I regularly run into the problem that I have a Python function that I want to use in both, eager and graph execution mode. I therefore have to adjust the code so that it can handle both situations. Here are two examples:
```python
import tensorflow as tf
```
```python
def lin_to_db(x: float | tf.Tensor) -> float | tf.Tensor:
# convert signal to noise ratio (SNR) from linear to dB
if tf.is_tensor(x):
return tf.math.log(x) * (10. / tf.math.log(10.))
else:
return math.log10(x) * 10.
```
```python
def cast_to_int_if_eager(x: tf.Variable) -> int | tf.Variable:
return int(x) if tf.executing_eagerly() else x
```
Are there best practices for such functions? Or maybe helpful predefined functions from Tensorflow?

python tensorflow tensorflow-eager-mode tensorflow-graph-mode tensorflow-function

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