/**
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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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import * as tf from '../index';
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import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
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describeWithFlags('basicLSTMCell', ALL_ENVS, () => {
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it('basicLSTMCell with batch=2', async () => {
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const lstmKernel = tf.randomNormal([3, 4]);
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const lstmBias = tf.randomNormal([4]);
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const forgetBias = tf.scalar(1.0);
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const data = tf.randomNormal([1, 2]);
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const batchedData = tf.concat2d([data, data], 0); // 2x2
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const c = tf.randomNormal([1, 1]);
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const batchedC = tf.concat2d([c, c], 0); // 2x1
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const h = tf.randomNormal([1, 1]);
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const batchedH = tf.concat2d([h, h], 0); // 2x1
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const [newC, newH] = tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH);
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const newCVals = await newC.array();
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const newHVals = await newH.array();
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expect(newCVals[0][0]).toEqual(newCVals[1][0]);
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expect(newHVals[0][0]).toEqual(newHVals[1][0]);
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});
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it('basicLSTMCell accepts a tensor-like object', async () => {
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const lstmKernel = tf.randomNormal([3, 4]);
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const lstmBias = [0, 0, 0, 0];
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const forgetBias = 1;
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const data = [[0, 0]]; // 1x2
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const batchedData = tf.concat2d([data, data], 0); // 2x2
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const c = [[0]]; // 1x1
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const batchedC = tf.concat2d([c, c], 0); // 2x1
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const h = [[0]]; // 1x1
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const batchedH = tf.concat2d([h, h], 0); // 2x1
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const [newC, newH] = tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH);
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const newCVals = await newC.array();
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const newHVals = await newH.array();
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expect(newCVals[0][0]).toEqual(newCVals[1][0]);
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expect(newHVals[0][0]).toEqual(newHVals[1][0]);
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});
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});
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describeWithFlags('basicLSTMCell throws with non-tensor', ALL_ENVS, () => {
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it('input: forgetBias', () => {
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const lstmKernel = tf.randomNormal([3, 4]);
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const lstmBias = tf.randomNormal([4]);
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const data = tf.randomNormal([1, 2]);
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const batchedData = tf.concat2d([data, data], 0); // 2x2
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const c = tf.randomNormal([1, 1]);
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const batchedC = tf.concat2d([c, c], 0); // 2x1
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const h = tf.randomNormal([1, 1]);
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const batchedH = tf.concat2d([h, h], 0); // 2x1
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expect(() => tf.basicLSTMCell({}, lstmKernel, lstmBias, batchedData, batchedC, batchedH))
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.toThrowError(/Argument 'forgetBias' passed to 'basicLSTMCell' must be a Tensor/);
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});
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it('input: lstmKernel', () => {
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const lstmBias = tf.randomNormal([4]);
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const forgetBias = tf.scalar(1.0);
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const data = tf.randomNormal([1, 2]);
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const batchedData = tf.concat2d([data, data], 0); // 2x2
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const c = tf.randomNormal([1, 1]);
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const batchedC = tf.concat2d([c, c], 0); // 2x1
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const h = tf.randomNormal([1, 1]);
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const batchedH = tf.concat2d([h, h], 0); // 2x1
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expect(() => tf.basicLSTMCell(forgetBias, {}, lstmBias, batchedData, batchedC, batchedH))
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.toThrowError(/Argument 'lstmKernel' passed to 'basicLSTMCell' must be a Tensor/);
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});
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it('input: lstmBias', () => {
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const lstmKernel = tf.randomNormal([3, 4]);
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const forgetBias = tf.scalar(1.0);
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const data = tf.randomNormal([1, 2]);
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const batchedData = tf.concat2d([data, data], 0); // 2x2
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const c = tf.randomNormal([1, 1]);
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const batchedC = tf.concat2d([c, c], 0); // 2x1
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const h = tf.randomNormal([1, 1]);
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const batchedH = tf.concat2d([h, h], 0); // 2x1
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expect(() => tf.basicLSTMCell(forgetBias, lstmKernel, {}, batchedData, batchedC, batchedH))
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.toThrowError(/Argument 'lstmBias' passed to 'basicLSTMCell' must be a Tensor/);
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});
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it('input: data', () => {
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const lstmKernel = tf.randomNormal([3, 4]);
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const lstmBias = tf.randomNormal([4]);
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const forgetBias = tf.scalar(1.0);
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const c = tf.randomNormal([1, 1]);
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const batchedC = tf.concat2d([c, c], 0); // 2x1
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const h = tf.randomNormal([1, 1]);
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const batchedH = tf.concat2d([h, h], 0); // 2x1
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expect(() => tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, {}, batchedC, batchedH))
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.toThrowError(/Argument 'data' passed to 'basicLSTMCell' must be a Tensor/);
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});
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it('input: c', () => {
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const lstmKernel = tf.randomNormal([3, 4]);
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const lstmBias = tf.randomNormal([4]);
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const forgetBias = tf.scalar(1.0);
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const data = tf.randomNormal([1, 2]);
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const batchedData = tf.concat2d([data, data], 0); // 2x2
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const h = tf.randomNormal([1, 1]);
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const batchedH = tf.concat2d([h, h], 0); // 2x1
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expect(() => tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, {}, batchedH))
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.toThrowError(/Argument 'c' passed to 'basicLSTMCell' must be a Tensor/);
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});
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it('input: h', () => {
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const lstmKernel = tf.randomNormal([3, 4]);
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const lstmBias = tf.randomNormal([4]);
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const forgetBias = tf.scalar(1.0);
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const data = tf.randomNormal([1, 2]);
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const batchedData = tf.concat2d([data, data], 0); // 2x2
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const c = tf.randomNormal([1, 1]);
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const batchedC = tf.concat2d([c, c], 0); // 2x1
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expect(() => tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, {}))
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.toThrowError(/Argument 'h' passed to 'basicLSTMCell' must be a Tensor/);
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});
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});
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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {Rank} from '../types';\n\ndescribeWithFlags('basicLSTMCell', ALL_ENVS, () => {\n  it('basicLSTMCell with batch=2', async () => {\n    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n    const lstmBias = tf.randomNormal<Rank.R1>([4]);\n    const forgetBias = tf.scalar(1.0);\n\n    const data = tf.randomNormal<Rank.R2>([1, 2]);\n    const batchedData = tf.concat2d([data, data], 0);  // 2x2\n    const c = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedC = tf.concat2d([c, c], 0);  // 2x1\n    const h = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedH = tf.concat2d([h, h], 0);  // 2x1\n    const [newC, newH] = tf.basicLSTMCell(\n        forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH);\n    const newCVals = await newC.array();\n    const newHVals = await newH.array();\n    expect(newCVals[0][0]).toEqual(newCVals[1][0]);\n    expect(newHVals[0][0]).toEqual(newHVals[1][0]);\n  });\n\n  it('basicLSTMCell accepts a tensor-like object', async () => {\n    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n    const lstmBias = [0, 0, 0, 0];\n    const forgetBias = 1;\n\n    const data = [[0, 0]];                             // 1x2\n    const batchedData = tf.concat2d([data, data], 0);  // 2x2\n    const c = [[0]];                                   // 1x1\n    const batchedC = tf.concat2d([c, c], 0);           // 2x1\n    const h = [[0]];                                   // 1x1\n    const batchedH = tf.concat2d([h, h], 0);           // 2x1\n    const [newC, newH] = tf.basicLSTMCell(\n        forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH);\n    const newCVals = await newC.array();\n    const newHVals = await newH.array();\n    expect(newCVals[0][0]).toEqual(newCVals[1][0]);\n    expect(newHVals[0][0]).toEqual(newHVals[1][0]);\n  });\n});\ndescribeWithFlags('basicLSTMCell throws with non-tensor', ALL_ENVS, () => {\n  it('input: forgetBias', () => {\n    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n    const lstmBias = tf.randomNormal<Rank.R1>([4]);\n\n    const data = tf.randomNormal<Rank.R2>([1, 2]);\n    const batchedData = tf.concat2d([data, data], 0);  // 2x2\n    const c = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedC = tf.concat2d([c, c], 0);  // 2x1\n    const h = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedH = tf.concat2d([h, h], 0);  // 2x1\n    expect(\n        () => tf.basicLSTMCell(\n            {} as tf.Scalar, lstmKernel, lstmBias, batchedData, batchedC,\n            batchedH))\n        .toThrowError(\n            /Argument 'forgetBias' passed to 'basicLSTMCell' must be a Tensor/);\n  });\n  it('input: lstmKernel', () => {\n    const lstmBias = tf.randomNormal<Rank.R1>([4]);\n    const forgetBias = tf.scalar(1.0);\n\n    const data = tf.randomNormal<Rank.R2>([1, 2]);\n    const batchedData = tf.concat2d([data, data], 0);  // 2x2\n    const c = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedC = tf.concat2d([c, c], 0);  // 2x1\n    const h = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedH = tf.concat2d([h, h], 0);  // 2x1\n    expect(\n        () => tf.basicLSTMCell(\n            forgetBias, {} as tf.Tensor2D, lstmBias, batchedData, batchedC,\n            batchedH))\n        .toThrowError(\n            /Argument 'lstmKernel' passed to 'basicLSTMCell' must be a Tensor/);\n  });\n  it('input: lstmBias', () => {\n    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n    const forgetBias = tf.scalar(1.0);\n\n    const data = tf.randomNormal<Rank.R2>([1, 2]);\n    const batchedData = tf.concat2d([data, data], 0);  // 2x2\n    const c = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedC = tf.concat2d([c, c], 0);  // 2x1\n    const h = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedH = tf.concat2d([h, h], 0);  // 2x1\n    expect(\n        () => tf.basicLSTMCell(\n            forgetBias, lstmKernel, {} as tf.Tensor1D, batchedData, batchedC,\n            batchedH))\n        .toThrowError(\n            /Argument 'lstmBias' passed to 'basicLSTMCell' must be a Tensor/);\n  });\n  it('input: data', () => {\n    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n    const lstmBias = tf.randomNormal<Rank.R1>([4]);\n    const forgetBias = tf.scalar(1.0);\n\n    const c = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedC = tf.concat2d([c, c], 0);  // 2x1\n    const h = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedH = tf.concat2d([h, h], 0);  // 2x1\n    expect(\n        () => tf.basicLSTMCell(\n            forgetBias, lstmKernel, lstmBias, {} as tf.Tensor2D, batchedC,\n            batchedH))\n        .toThrowError(\n            /Argument 'data' passed to 'basicLSTMCell' must be a Tensor/);\n  });\n  it('input: c', () => {\n    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n    const lstmBias = tf.randomNormal<Rank.R1>([4]);\n    const forgetBias = tf.scalar(1.0);\n\n    const data = tf.randomNormal<Rank.R2>([1, 2]);\n    const batchedData = tf.concat2d([data, data], 0);  // 2x2\n    const h = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedH = tf.concat2d([h, h], 0);  // 2x1\n    expect(\n        () => tf.basicLSTMCell(\n            forgetBias, lstmKernel, lstmBias, batchedData, {} as tf.Tensor2D,\n            batchedH))\n        .toThrowError(\n            /Argument 'c' passed to 'basicLSTMCell' must be a Tensor/);\n  });\n  it('input: h', () => {\n    const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n    const lstmBias = tf.randomNormal<Rank.R1>([4]);\n    const forgetBias = tf.scalar(1.0);\n\n    const data = tf.randomNormal<Rank.R2>([1, 2]);\n    const batchedData = tf.concat2d([data, data], 0);  // 2x2\n    const c = tf.randomNormal<Rank.R2>([1, 1]);\n    const batchedC = tf.concat2d([c, c], 0);  // 2x1\n    expect(\n        () => tf.basicLSTMCell(\n            forgetBias, lstmKernel, lstmBias, batchedData, batchedC,\n            {} as tf.Tensor2D))\n        .toThrowError(\n            /Argument 'h' passed to 'basicLSTMCell' must be a Tensor/);\n  });\n});\n"]}
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