"use strict";
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/**
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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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Object.defineProperty(exports, "__esModule", { value: true });
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exports.stepConfig = void 0;
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var tfjs_1 = require("@tensorflow/tfjs");
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var nodejs_kernel_backend_1 = require("../nodejs_kernel_backend");
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exports.stepConfig = {
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kernelName: tfjs_1.Step,
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backendName: 'tensorflow',
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kernelFunc: function (args) {
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var x = args.inputs.x;
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var backend = args.backend;
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var alpha = args.attrs.alpha;
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var dtype = x.dtype;
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return (0, tfjs_1.tidy)(function () {
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var nans = (0, tfjs_1.isNaN)(x);
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var stepNoNans = (0, tfjs_1.where)((0, tfjs_1.greater)(x, (0, tfjs_1.scalar)(0, dtype)), (0, tfjs_1.ones)(x.shape), (0, tfjs_1.fill)(x.shape, alpha, dtype));
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var opAttrs = [(0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('T', tfjs_1.backend_util.upcastType(x.dtype, stepNoNans.dtype))];
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return backend.executeSingleOutput('Select', opAttrs, [nans, x, stepNoNans]);
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});
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}
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};
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