/**
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* @license
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* Copyright 2018 Google Inc. 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 { Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
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import { TensorLike } from '../types';
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/**
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* Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.
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*
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* For example, if:
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* A: shape(3) = |r1, g1, b1|
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* B: shape(2) = |r2, g2|
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* C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|
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*
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* @param tensors A list of`tf.Tensor`s to concatenate.
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* @return The concatenated array.
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*/
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declare function concat1d_(tensors: Array<Tensor1D | TensorLike>): Tensor1D;
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/**
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* Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.
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*
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* For example, if:
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* A: shape(2, 3) = | r1, g1, b1 |
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* | r2, g2, b2 |
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*
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* B: shape(2, 3) = | r3, g3, b3 |
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* | r4, g4, b4 |
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*
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* C = tf.concat2d([A, B], axis)
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*
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* if axis = 0:
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* C: shape(4, 3) = | r1, g1, b1 |
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* | r2, g2, b2 |
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* | r3, g3, b3 |
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* | r4, g4, b4 |
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*
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* if axis = 1:
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* C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |
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* | r2, g2, b2, r4, g4, b4 |
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*
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*
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* @param tensors A list of `tf.Tensor`s to concatenate.
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* @param axis The axis to concatenate along.
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* @return The concatenated array.
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*/
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declare function concat2d_(tensors: Array<Tensor2D | TensorLike>, axis: number): Tensor2D;
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/**
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* Concatenates a list of `tf.Tensor3D`s along an axis.
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* See `concat` for details.
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*
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* For example, if:
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* A: shape(2, 1, 3) = | r1, g1, b1 |
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* | r2, g2, b2 |
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*
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* B: shape(2, 1, 3) = | r3, g3, b3 |
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* | r4, g4, b4 |
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*
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* C = tf.concat3d([A, B], axis)
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*
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* if axis = 0:
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* C: shape(4, 1, 3) = | r1, g1, b1 |
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* | r2, g2, b2 |
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* | r3, g3, b3 |
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* | r4, g4, b4 |
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*
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* if axis = 1:
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* C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |
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* | r2, g2, b2, r4, g4, b4 |
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*
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* if axis = 2:
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* C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |
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* | r2, g2, b2, r4, g4, b4 |
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*
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* @param tensors A list of`tf.Tensor`s to concatenate.
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* @param axis The axis to concate along.
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* @return The concatenated array.
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*/
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declare function concat3d_(tensors: Array<Tensor3D | TensorLike>, axis: number): Tensor3D;
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/**
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* Concatenates a list of `tf.Tensor4D`s along an axis.
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* See `concat` for details.
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*
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* @param tensors A list of `tf.Tensor`s to concatenate.
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* @param axis The axis to concate along.
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* @return The concatenated array.
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*/
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declare function concat4d_(tensors: Array<Tensor4D | TensorLike>, axis: number): Tensor4D;
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/**
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* Concatenates a list of `tf.Tensor`s along a given axis.
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*
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* The tensors ranks and types must match, and their sizes must match in all
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* dimensions except `axis`.
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*
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* Also available are stricter rank-specific methods that assert that
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* `tensors` are of the given rank:
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* - `tf.concat1d`
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* - `tf.concat2d`
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* - `tf.concat3d`
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* - `tf.concat4d`
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*
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* Except `tf.concat1d` (which does not have axis param), all methods have
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* same signature as this method.
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*
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* ```js
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* const a = tf.tensor1d([1, 2]);
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* const b = tf.tensor1d([3, 4]);
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* a.concat(b).print(); // or a.concat(b)
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* ```
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*
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* ```js
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* const a = tf.tensor1d([1, 2]);
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* const b = tf.tensor1d([3, 4]);
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* const c = tf.tensor1d([5, 6]);
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* tf.concat([a, b, c]).print();
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* ```
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*
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* ```js
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* const a = tf.tensor2d([[1, 2], [10, 20]]);
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* const b = tf.tensor2d([[3, 4], [30, 40]]);
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* const axis = 1;
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* tf.concat([a, b], axis).print();
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* ```
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* @param tensors A list of tensors to concatenate.
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* @param axis The axis to concate along. Defaults to 0 (the first dim).
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*/
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/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
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declare function concat_<T extends Tensor>(tensors: Array<T | TensorLike>, axis?: number): T;
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/**
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* Splits a `tf.Tensor` into sub tensors.
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*
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* If `numOrSizeSplits` is a number, splits `x` along dimension `axis`
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* into `numOrSizeSplits` smaller tensors.
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* Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.
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*
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* If `numOrSizeSplits` is a number array, splits `x` into
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* `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the
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* same size as `x` except along dimension `axis` where the size is
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* `numOrSizeSplits[i]`.
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
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* const [a, b] = tf.split(x, 2, 1);
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* a.print();
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* b.print();
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*
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* const [c, d, e] = tf.split(x, [1, 2, 1], 1);
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* c.print();
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* d.print();
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* e.print();
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* ```
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*
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* @param x The input tensor to split.
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* @param numOrSizeSplits Either an integer indicating the number of
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* splits along the axis or an array of integers containing the sizes of
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* each output tensor along the axis. If a number then it must evenly divide
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* `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.
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* @param axis The dimension along which to split. Defaults to 0 (the first
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* dim).
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*/
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/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
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declare function split_<T extends Tensor>(x: T | TensorLike, numOrSizeSplits: number[] | number, axis?: number): T[];
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export declare const concat: typeof concat_;
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export declare const concat1d: typeof concat1d_;
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export declare const concat2d: typeof concat2d_;
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export declare const concat3d: typeof concat3d_;
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export declare const concat4d: typeof concat4d_;
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export declare const split: typeof split_;
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export {};
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