However, to maintain variable-scope based variable reuse
you will need to combine it with
tf.compat.v1.keras.utils.track_tf1_style_variables. (Though
it will behave as if reuse is always set to tf.compat.v1.AUTO_REUSE.)
The TF2 equivalent, if you are just trying to track
variable name prefixes and not control get_variable-based variable reuse,
would be to use tf.name_scope and capture the output of opening the
scope (which represents the current name prefix).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.compat.v1.get_variable_scope\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/variable_scope.py#L1415-L1445) |\n\nReturns the current variable scope. \n\n tf.compat.v1.get_variable_scope()\n\n\u003cbr /\u003e\n\nMigrate to TF2\n--------------\n\n\u003cbr /\u003e\n\n| **Caution:** This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.\n\nAlthough it is a legacy [`compat.v1`](../../../tf/compat/v1) api,\n[`tf.compat.v1.get_variable`](../../../tf/compat/v1/get_variable) is compatible with eager\nexecution and [`tf.function`](../../../tf/function)\n\nHowever, to maintain variable-scope based variable reuse\nyou will need to combine it with\n`tf.compat.v1.keras.utils.track_tf1_style_variables`. (Though\nit will behave as if reuse is always set to [`tf.compat.v1.AUTO_REUSE`](../../../tf/compat/v1#AUTO_REUSE).)\n\nSee the\n[migration guide](https://www.tensorflow.org/guide/migrate/model_mapping)\nfor more info.\n\nThe TF2 equivalent, if you are just trying to track\nvariable name prefixes and not control `get_variable`-based variable reuse,\nwould be to use [`tf.name_scope`](../../../tf/name_scope) and capture the output of opening the\nscope (which represents the current name prefix).\n\nFor example: \n\n x = tf.name_scope('foo') as current_scope:\n ...\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nDescription\n-----------"]]