@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
/**
* @license
* Copyright 2020 Google Inc. 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 kernel_names_1 = require("../kernel_names");
var tensor_util_env_1 = require("../tensor_util_env");
var operation_1 = require("./operation");
/**
* Pads a `tf.Tensor` with a given value and paddings.
*
* This operation currently only implements the `CONSTANT` mode.
*
* Also available are stricter rank-specific methods with the same signature
* as this method that assert that `paddings` is of given length.
* - `tf.pad1d`
* - `tf.pad2d`
* - `tf.pad3d`
* - `tf.pad4d`
*
* ```js
* const x = tf.tensor1d([1, 2, 3, 4]);
* x.pad([[1, 2]]).print();
* ```
* @param x The tensor to pad.
* @param paddings An array of length `R` (the rank of the tensor), where
* each element is a length-2 tuple of ints `[padBefore, padAfter]`,
* specifying how much to pad along each dimension of the tensor.
* @param constantValue The pad value to use. Defaults to 0.
*/
/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
function pad_(x, paddings, constantValue) {
if (constantValue === void 0) { constantValue = 0; }
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'pad');
if ($x.rank === 0) {
throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');
}
var forward = function (backend, save) {
save([$x]);
return backend.pad($x, paddings, constantValue);
};
var attrs = { paddings: paddings, constantValue: constantValue };
var inputs = { x: $x };
return engine_1.ENGINE.runKernelFunc(forward, inputs, null /* grad */, kernel_names_1.PadV2, attrs);
}
exports.pad = operation_1.op({ pad_: pad_ });
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