ijvdZddlZddlmZddlmZddlmZddlm Z ejZ dZ d dZy) zCProvides a proper python API for the symbols exported through swig.N) graph_pb2) config_pb2)_pywrap_tf_optimizer)clusterTc zt|tjs!tdt |j t r!tj}|j}ntj}|&||j|j||||}nZt5tj} ||j|j||||}|j dddt rt j"j%}S#|jwxYw#1swYExYw)a Optimize the provided metagraph. For best results, the signature_def field in `metagraph` should be populated with information about input (feed) and output (fetch) tensors. Args: config_proto: a ConfigProto protobuf. metagraph: a MetagraphDef protobuf. verbose: whether to log optimization results. graph_id: a string identifying this graph. cluster: a grappler cluster object representing hardware resources available to run this graph. strip_default_attributes: whether graph node attributes having default values should be removed after all the optimization passes. This option is useful if the resulting graph will be executed by an older process that might not know some of the recently added attributes. zCArgument `config_proto` should be a tf.ConfigProto, received type: N) isinstancer ConfigProto TypeErrortype__name__is_osstf_optTF_OptimizeGraphSerializedSerializeToStringTF_OptimizeGraph tf_cluster_OPTIMIZE_GRAPH_CLUSTER_LOCKgclusterClusterShutdownrGraphDef FromString) config_proto metagraphverbosegraph_idrstrip_default_attributesoptimize_method out_graphs |/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/tensorflow/python/grappler/tf_optimizer.py OptimizeGraphr!s.. L*"8"8 9 &&*<&8&A&A%BD EE 77O++-I--O &&( I &  "g #     * * ,    $     ""--i8I  s$!D17%DD1D..D11D:)Tsgraph_to_optimizeNF)__doc__ threadingtensorflow.core.frameworkrtensorflow.core.protobufrtensorflow.python.grapplerrrrrLockrr r!r r*sAJ//E:-y~~/  /+0 ?r)