/**
 * @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.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-core/dist/ops/log_softmax" />
import { Tensor } from '../tensor';
import { TensorLike } from '../types';
/**
 * Computes the log softmax.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 *
 * a.logSoftmax().print();  // or tf.logSoftmax(a)
 * ```
 *
 * ```js
 * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
 *
 * a.logSoftmax().print();  // or tf.logSoftmax(a)
 * ```
 *
 * @param logits The logits array.
 * @param axis The dimension softmax would be performed on. Defaults to `-1`
 *     which indicates the last dimension.
 *
 * @doc {heading: 'Operations', subheading: 'Normalization'}
 */
declare function logSoftmax_<T extends Tensor>(logits: T | TensorLike, axis?: number): T;
export declare const logSoftmax: typeof logSoftmax_;
export {};