/**

* @license

* Copyright 2020 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.

* =============================================================================

*/

import {customGrad} from '../../gradients';

import {Tensor} from '../../tensor';

import {GradSaveFunc} from '../../tensor_types';

import {convertToTensor} from '../../tensor_util_env';

import {TensorLike} from '../../types';

import {assertShapesMatch} from '../../util';

import {add} from '../add';

import {expandShapeToKeepDim} from '../axis_util';

import {cast} from '../cast';

import {div} from '../div';

import {exp} from '../exp';

import {logSumExp} from '../log_sum_exp';

import {Reduction} from '../loss_ops_utils';

import {mul} from '../mul';

import {neg} from '../neg';

import {op} from '../operation';

import {reshape} from '../reshape';

import {scalar} from '../scalar';

import {sub} from '../sub';

import {sum} from '../sum';

import {computeWeightedLoss} from './compute_weighted_loss';

/**

* Computes softmax cross entropy between logits and labels.

*

* Measures the probability error in discrete classification tasks in which

* the classes are mutually exclusive (each entry is in exactly one class).

* For example, each CIFAR-10 image is labeled with one and only one label: an

* image can be a dog or a truck, but not both.

*

* `NOTE`: While the classes are mutually exclusive, their probabilities need

* not be. All that is required is that each row of labels is a valid

* probability distribution. If they are not, the computation of the gradient

* will be incorrect.

*

* `WARNING`: This op expects unscaled logits, since it performs a softmax on

* logits internally for efficiency. Do not call this op with the output of

* softmax, as it will produce incorrect results.

*

* logits and labels must have the same shape, e.g. [batch_size, num_classes]

* and the same dtype.

* @param labels The labels array.

* @param logits The logits array.

* @param dim The dimension softmax would be performed on. Defaults to `-1`

* which indicates the last dimension.

*/

function softmaxCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(

}

/**

* Computes the softmax cross entropy loss between two tensors.

*

* If labelSmoothing is nonzero, smooth the labels towards 1/2:

*

* newOnehotLabels = onehotLabels * (1 - labelSmoothing)

* + labelSmoothing / numClasses

*

* @param onehotLabels One hot encoded labels

* [batch_size, num_classes], same dimensions as 'predictions'.

* @param logits The predicted outputs.

* @param weights Tensor whose rank is either 0, or 1, and must be

* broadcastable to `loss` of shape [batch_size]

* @param labelSmoothing If greater than 0, then smooth the labels.

* @param reduction Type of reduction to apply to loss. Should be of type

* `Reduction`

*

* @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }

*/

function softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(