/**
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* @license
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* Copyright 2019 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 {ENGINE} from '../engine';
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import {Tensor} from '../tensor';
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import {convertToTensor} from '../tensor_util_env';
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import {op} from './operation';
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/**
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* Returns a diagonal tensor with a given diagonal values.
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*
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* Given a diagonal, this operation returns a tensor with the diagonal and
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* everything else padded with zeros.
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*
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* Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor
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* of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3, 4]);
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*
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* tf.diag(x).print()
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* ```
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* ```js
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* const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])
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*
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* tf.diag(x).print()
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* ```
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* @param x The input tensor.
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*/
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function diag_(x: Tensor): Tensor {
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const $x = convertToTensor(x, 'x', 'diag').flatten();
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const outShape = [...x.shape, ...x.shape];
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return ENGINE.runKernelFunc(backend => backend.diag($x), {$x})
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.reshape(outShape);
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}
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export const diag = op({diag_});
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