/**
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* @license
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* Copyright 2020 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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/// <amd-module name="@tensorflow/tfjs-core/dist/ops/mirror_pad" />
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import { Tensor } from '../tensor';
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import { TensorLike } from '../types';
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/**
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* Pads a `tf.Tensor` using mirror padding.
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*
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* This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.
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*
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* ```js
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* const x = tf.range(0, 9).reshape([1, 1, 3, 3]);
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* x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();
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* ```
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* @param x The tensor to pad.
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* @param paddings An array of length `R` (the rank of the tensor), where
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* each element is a length-2 tuple of ints `[padBefore, padAfter]`,
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* specifying how much to pad along each dimension of the tensor.
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* In "reflect" mode, the padded regions do not include the borders,
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* while in "symmetric" mode the padded regions do include the borders.
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* For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,
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* then the output is `[1, 2, 3, 2, 1]` in "reflect" mode, and
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* `[1, 2, 3, 3, 2]` in "symmetric" mode.
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* If `mode` is "reflect" then both `paddings[D, 0]` and `paddings[D, 1]`
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* must be no greater than `x.shape[D] - 1`. If mode is "symmetric"
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* then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than
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* `x.shape[D]`
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* @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`
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*/
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/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
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declare function mirrorPad_<T extends Tensor>(x: T | TensorLike, paddings: Array<[number, number]>, mode: 'reflect' | 'symmetric'): T;
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export declare const mirrorPad: typeof mirrorPad_;
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export {};
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