o ®Ÿ?e ã@s<dZddlmZddlmZdaedƒdd„ƒZdd „Zd S) zPython debug mode enabler.é)Úcontext)Ú tf_exportFz#data.experimental.enable_debug_modecCst ¡r tdƒdStdƒ‚)aEnables debug mode for tf.data. Example usage with pdb module: ``` import tensorflow as tf import pdb tf.data.experimental.enable_debug_mode() def func(x): # Python 3.7 and older requires `pdb.Pdb(nosigint=True).set_trace()` pdb.set_trace() x = x + 1 return x dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3]) dataset = dataset.map(func) for item in dataset: print(item) ``` The effect of debug mode is two-fold: 1) Any transformations that would introduce asynchrony, parallelism, or non-determinism to the input pipeline execution will be forced to execute synchronously, sequentially, and deterministically. 2) Any user-defined functions passed into tf.data transformations such as `map` will be wrapped in `tf.py_function` so that their body is executed "eagerly" as a Python function as opposed to a traced TensorFlow graph, which is the default behavior. Note that even when debug mode is enabled, the user-defined function is still traced to infer the shape and type of its outputs; as a consequence, any `print` statements or breakpoints will be triggered once during the tracing before the actual execution of the input pipeline. NOTE: As the debug mode setting affects the construction of the tf.data input pipeline, it should be enabled before any tf.data definitions. Raises: ValueError: When invoked from graph mode. Tz5`enable_debug_mode() is only supported in eager mode.N)rZexecuting_eagerlyÚtoggle_debug_modeÚ ValueError©rrúf/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/data/ops/debug_mode.pyÚenable_debug_modes- rcCs|adS)N)Ú DEBUG_MODE)Ú debug_moderrrrKsrN)Ú__doc__Ztensorflow.python.eagerrZ tensorflow.python.util.tf_exportrr rrrrrrÚs    2