/**

* @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 {Scalar, Tensor} from '../../tensor';

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

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

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

import {greaterEqual} from '../greater_equal';

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

import {lessEqual} from '../less_equal';

import {logicalAnd} from '../logical_and';

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

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

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

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

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

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

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

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

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

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

/**

* Copy a tensor setting everything outside a central band in each innermost

* matrix to zero.

*

* The band part is computed as follows: Assume input has `k` dimensions

* `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where

* `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.

* The indicator function

* `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)`

* `&& (num_upper < 0 || (n-m) <= num_upper)`

*

* ```js

* const x = tf.tensor2d([[ 0, 1, 2, 3],

* [-1, 0, 1, 2],

* [-2, -1, 0, 1],

* [-3, -2, -1, 0]]);

* let y = tf.linalg.bandPart(x, 1, -1);

* y.print(); // [[ 0, 1, 2, 3],

* // [-1, 0, 1, 2],

* // [ 0, -1, 0, 1],

* // [ 0, 0 , -1, 0]]

* let z = tf.linalg.bandPart(x, 2, 1);

* z.print(); // [[ 0, 1, 0, 0],

* // [-1, 0, 1, 0],

* // [-2, -1, 0, 1],

* // [ 0, -2, -1, 0]]

* ```

*

* @param x Rank `k` tensor

* @param numLower Number of subdiagonals to keep.

* If negative, keep entire lower triangle.

* @param numUpper Number of subdiagonals to keep.

* If negative, keep entire upper triangle.

* @returns Rank `k` tensor of the same shape as input.

* The extracted banded tensor.

*

* @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}

*/

function bandPart_<T extends Tensor>(

}

export const bandPart = /* @__PURE__ */ op({bandPart_});