@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
var engine_1 = require("../engine");
var tensor_util_env_1 = require("../tensor_util_env");
var operation_1 = require("./operation");
/**
* Returns a diagonal tensor with a given diagonal values.
*
* Given a diagonal, this operation returns a tensor with the diagonal and
* everything else padded with zeros.
*
* Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor
* of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`
*
* ```js
* const x = tf.tensor1d([1, 2, 3, 4]);
*
* tf.diag(x).print()
* ```
* ```js
* const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])
*
* tf.diag(x).print()
* ```
* @param x The input tensor.
*/
function diag_(x) {
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'diag').flatten();
var outShape = x.shape.concat(x.shape);
return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.diag($x); }, { $x: $x })
.reshape(outShape);
}
exports.diag = operation_1.op({ diag_: diag_ });
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