tf.function has inherent limitations including issues with executing Python side effects like printing and modifying globals. These side effects can result in unexpected behavior, only executing the first time a set of inputs is called. After tracing, multiple invocations will not run the Python code again. To properly execute code with every call, use TensorFlow APIs like tf.data or tf.print. When necessary, tf.py_function can be an alternative, although it has portability and performance drawbacks, particularly in distributed environments.
The outputs of a tf.function must be return values, and side effects may behave unexpectedly, executing only for the first call with a set of inputs.
To ensure code executes with each call within tf.function, rely on TensorFlow APIs like tf.data, tf.print, and tf.Variable.assign instead of Python side effects.
Using tf.py_function allows for executing Python code during each invocation of a tf.function, but it comes with limitations such as lack of portability and performance concerns.
When tracing with tf.function, the first call executes the Python code, while subsequent calls only execute the traced graph without running the original Python logic.
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