/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import { Shape } from '@tensorflow/tfjs';
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/**
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* Node.js-specific tensor type: int64-type scalar.
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*
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* This class is created for a specific purpose: to support
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* writing `step`s to TensorBoard via op-kernel bindings.
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* `step` is required to have an int64 dtype, but TensorFlow.js
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* (tfjs-core) doesn't have a built-in int64 dtype. This is
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* related to a lack of `Int64Array` or `Uint64Array` typed
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* array in basic JavaScript.
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*
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* This class is introduced as a workaround.
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*/
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export declare class Int64Scalar {
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readonly value: number;
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readonly dtype: string;
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readonly rank: number;
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private valueArray_;
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private static endiannessOkay_;
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constructor(value: number);
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get shape(): Shape;
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/** Get the Int32Array that represents the int64 value. */
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get valueArray(): Int32Array;
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}
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/**
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* This method encodes a Int32Array as Int64 layout in order to create TF_INT64
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* tensor through binding.
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*/
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export declare function encodeInt32ArrayAsInt64(value: Int32Array): Int32Array;
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