Stay organized with collections
Save and categorize content based on your preferences.
tensorflow::ops::BarrierInsertMany
#include <data_flow_ops.h>
For each key, assigns the respective value to the specified component.
Summary
If a key is not found in the barrier, this operation will create a new incomplete element. If a key is found in the barrier, and the element already has a value at component_index, this operation will fail with INVALID_ARGUMENT, and leave the barrier in an undefined state.
Arguments:
- scope: A Scope object
- handle: The handle to a barrier.
- keys: A one-dimensional tensor of keys, with length n.
- values: An any-dimensional tensor of values, which are associated with the respective keys. The 0th dimension must have length n.
- component_index: The component of the barrier elements that is being assigned.
Returns:
Public attributes
Public functions
operator::tensorflow::Operation
operator::tensorflow::Operation() const
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-04-20 UTC.
[null,null,["Last updated 2020-04-20 UTC."],[],[],null,["# tensorflow::ops::BarrierInsertMany Class Reference\n\ntensorflow::ops::BarrierInsertMany\n==================================\n\n`#include \u003cdata_flow_ops.h\u003e`\n\nFor each key, assigns the respective value to the specified component.\n\nSummary\n-------\n\nIf a key is not found in the barrier, this operation will create a new incomplete element. If a key is found in the barrier, and the element already has a value at component_index, this operation will fail with INVALID_ARGUMENT, and leave the barrier in an undefined state.\n\nArguments:\n\n- scope: A [Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- handle: The handle to a barrier.\n- keys: A one-dimensional tensor of keys, with length n.\n- values: An any-dimensional tensor of values, which are associated with the respective keys. The 0th dimension must have length n.\n- component_index: The component of the barrier elements that is being assigned.\n\n\u003cbr /\u003e\n\nReturns:\n\n- the created [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation)\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [BarrierInsertMany](#classtensorflow_1_1ops_1_1_barrier_insert_many_1a8ee6cfc13fdf57f11e86b4d6692898b8)`(const ::`[tensorflow::Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` handle, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` keys, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` values, int64 component_index)` ||\n\n| ### Public attributes ||\n|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_barrier_insert_many_1aa3425272e66a4448615caa6b258f4a66) | [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n\n| ### Public functions ||\n|----------------------------------------------------------------------------------------------------------------------------------|---------|\n| [operator::tensorflow::Operation](#classtensorflow_1_1ops_1_1_barrier_insert_many_1ad8ad6d9598344b4090c7d2af0ace852d)`() const ` | ` ` ` ` |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\nPublic functions\n----------------\n\n### BarrierInsertMany\n\n```gdscript\n BarrierInsertMany(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input handle,\n ::tensorflow::Input keys,\n ::tensorflow::Input values,\n int64 component_index\n)\n``` \n\n### operator::tensorflow::Operation\n\n```gdscript\n operator::tensorflow::Operation() const \n```"]]